Most private loans are negotiated directly between a borrower and a small group of nonbank lenders, do not trade in a secondary market, and are typically held until maturity or refinancing. Price discovery is therefore episodic. Volatility appears lower, not necessarily because the underlying businesses are any safer, but because the loans are not forced through a public screen every second of the day. In calm periods, it looks resilient. In stressed periods, less so.

That dynamic explains why the first real tremors in private credit tend to show up outside the loans themselves. Commonly today, they are appearing in listed business development companies (BDCs), in discounts to net asset value, and in the share prices of listed alternative asset managers. Software stocks, which account for a larger sector weighting in private credit, fell almost 30% between October 2025 and February 2026 (SaaSpocalypse), while BDC shares fell about 10% on average, and discounts to NAV deepened. Public wrappers have started doing the price discovery that the private loans themselves do not.

It is also important to keep some restraint. Private credit is not a deposit-funded banking system. Nonbank private credit lenders generally have limited asset-liability mismatches and modest leverage compared with banks, while redemption gates and lockups can reduce the risk of fire sales. Even now, some large market participants argue that current strains are more liquidity- and rate-driven than evidence of a systemic default cycle.

The reason this debate has sharpened (and the purpose of this primer) is software. A growing share of private credit is now effectively exposure to sponsor-backed software businesses, concentrating risk in a sector whose assumptions are beginning to be tested.

What follows in the first part of this primer is as straightforward as we can make it. Strip private credit back to basics, map the money, and explain why the model looked so good for so long. Then we explore why software sits much closer to the centre of the story than many investors appreciated, and how the stress travels if conditions worsen.

Table of Contents

1. Private Credit Primer
   1.1 What Private Credit Actually Is
   1.2 Who the Players Are and How the Money Flows
   1.3 Why It Worked
   1.4 The Bridge to the Fault Line

2. Software Stress Test
   2.1 This Is No GFC Call
   2.2 The Market Is Already Speaking
   2.3 Why Software Is the Pressure Point
   2.4 Plumbing Matters More Than the Headline
   2.5 How the Stress Travels
   2.6 A Banking Spillover Looks Limited But Real
   2.7 What to Actually Worry About

Subscribe now

What Private Credit Actually Is

The cleanest way to define private credit is functionally rather than academically. It is credit that is originated, structured and held by lenders. It is debt-like, non-publicly traded instruments provided by nonbank entities such as private credit funds and BDCs. In practice, when most investors talk about private credit, they usually mean direct lending: a nonbank lender makes a loan (or a small club of lenders makes a loan) directly to a company and keeps that exposure on its balance sheet rather than distributing it across a broad syndicated market. Direct lending is also the dominant sleeve of the asset class, accounting for roughly 54% of global private credit AUM2.

That sounds simple, but the distinction from public credit is important. A broadly syndicated loan or a high-yield bond must be marketed to a wider investor base, often with ratings, fuller disclosure, a syndication timetable, and the risk that market conditions change before a deal clears. Direct lenders sell something else: speed, confidentiality, certainty of execution, bespoke terms, and a smaller lender group on the other side of the table. Borrowers are willing to pay a premium for that. In short, fewer disclosure requirements and far fewer lenders.

The instruments themselves are fairly recognisable. Almost all private credit loans are senior secured and floating-rate, usually priced at a spread over a benchmark such as SOFR. Direct lending loans are typically senior in the capital structure and often built around financial covenants. The point, from the lender’s perspective, is to sit high in the capital stack, collect current income, and avoid unnecessary duration risk.

Historically, the core borrower was a middle-market company (EBITDA between $25 million and $100 million), though the borrower profile continues to evolve alongside the broader growth of the asset class, with some companies above $100 million in EBITDA and loan sizes above $1 billion (jumbo loans). Among the growth in borrower type has been an increase in these “jumbo” loans, and, in recent years, the count of sponsor-backed direct lending deals in US dollars has exceeded that of the broadly syndicated loan market. Private credit has moved from a niche funding source for undersized companies to increasingly compete with public leveraged finance markets for mainstream sponsor-backed deals.

Sponsor backing3 is central to the machine, which accounts for roughly 80% of the direct lending market. Private credit is especially well-suited to leveraged buyouts, add-on acquisitions, recapitalisations and refinancings, where a sponsor values certainty and a lender values control. Put differently, private credit finances more than just organic corporate capex, and can often finance ownership structures, too. The borrower may be a company, but the real counterparties are frequently the sponsor and the lender.

So, what problem is private credit solving that banks no longer want to?

The answer is not mysterious. A large part of post-crisis nonbank lending reflects regulatory constraints on banks’ ability to lend to unprofitable and highly leveraged borrowers. US commercial banks have halved in number since 1998; the top 25 now hold more than half of all C&I loans (commercial and industrial), and balance-sheet priorities have shifted away from smaller, riskier corporates. Private credit stepped into that financing gap, then discovered it could do much more than fill it.

Who the Players Are and How Money Flows

There is a tendency to treat private credit as a single pool of capital. It is better understood as a chain. At the centre sits a relatively concentrated group of managers. Some estimates state the top 10 US private debt managers hold roughly 40%–45% of industry dry powder4. That helps explain why public sentiment around the asset class often concentrates in a handful of listed names (Ares Management, Apollo Global Management, Blackstone, Blue Owl Capital, BlackRock) when software concerns or redemption fears spill into equity markets. A market that looks private at the asset level still has very public transmission channels.

Behind those managers sits the LP base. The classic closed-end private credit fund still draws most of its capital from institutional allocators: pension funds, insurers, sovereign wealth funds, family offices, and high-net-worth investors. Private credit investment funds represent about $800 billion of the US market and are typically structured so that LP capital is locked up until loans are repaid, often over five to seven years. The attraction is floating-rate income, diversification, and lower observed volatility than public bonds.

One example of this came during the March 2023 episode of banking stress and public credit market volatility, when an increasing number of large companies and their private equity sponsors preferred to borrow from private credit lenders, given the certainty of loan execution.

Then there are BDCs, which matter disproportionately because they bring retail capital into an otherwise opaque market. BDC assets are roughly $500 billion of the US private credit market. These companies must distribute at least 90% of income to shareholders, most of whom are retail and high-net-worth investors. Non-traded BDCs raise equity, pair it with leverage and then lend mainly to mid-sized companies, while offering investors periodic liquidity windows that are usually capped (5% per quarter). That makes BDCs both a funding channel and a pressure point, and they are one of the clearest ways for public market sentiment to force itself onto private credit.

At the borrower end, the ecosystem is dominated by sponsor-backed mid-sized companies using debt for buyouts, acquisitions, and refinancing. Increasingly, a meaningful share of those borrowers are in software or software-adjacent sectors. Around one-third of private credit funds have extended loans to SaaS firms, on top of their rising exposure to big US tech firms and other artificial intelligence (AI) companies, while another estimate measures broader direct lending to software and technology as around a fifth of total debt exposure. That is why the asset class cannot be properly analysed by talking about “middle market” in the abstract. Much of the collateral is actually exposure to a specific ownership model (private equity) and a specific business model (recurring-revenue software).

Even the risk is not fully warehoused outside the banking system. Activities and related risks of banks and private credit funds are intimately interwoven5, such that under an extreme economic scenario, stress in the private credit industry could affect the banking system. In some cases, banks now fund the funds, warehouse the leverage, or provide the lines and financing infrastructure around the loans rather than the loans themselves.

Why It Worked

Private credit’s golden decade was not an accident. It was built on an unusually powerful alignment of macro and market structure. The footprint of private credit is larger in countries with lower policy rates and more stringent banking regulation, and the post-GFC regulation accelerated banks’ retreat from smaller and riskier borrowers. When policy rates were low for years, and bank balance sheets became more selective, private credit could offer borrowers speed and certainty while offering investors incremental yield. It was exactly the right product for exactly the right regime.

The LP side of the story is just as important. Fundraising demand is explicitly linked to a period of historically low interest rates and the associated search for higher returns. Pension funds, insurers, and other institutional investors were drawn to an appeal of higher expected returns and lower observed volatility than public bonds. In plain English, ZIRP (zero interest rate policy) created a shortage of income, and private credit looked like a way to manufacture it without taking public-market mark-to-market pain.

The operating environment then helped the model look even better. Low default rates over most of the past decade reflected low interest rates, regular covenant monitoring, and the ability to renegotiate flexibly with a relatively small lender group when borrowers came under pressure. That last point is key. A small club of lenders can amend, extend, or restructure far more quietly than a dispersed public market. A period of cheap money, therefore, did not merely reduce defaults mechanically but also gave sponsors and lenders time and optionality.

There was also a structural pull from the public markets themselves. High-yield bonds and leveraged loans increasingly serve larger borrowers, with average new issue sizes often too large for most middle-market firms, while companies have stayed private for longer and private equity hold periods have lengthened. As private credit funds grew, their average fund sizes rose too, allowing them to write larger cheques and compete for bigger deals. The result was a natural expansion from a niche lender to undersized borrowers to a third major funding channel alongside banks and public credit markets.

Software was almost the perfect borrower for that world. From a lender’s perspective, the pitch was compelling: recurring revenue, sticky customers, high gross margins, low capital intensity, strong sponsor backing, and products embedded in enterprise workflows. The most resilient software businesses tend to be mission-critical systems of record with long sales cycles, proprietary data, and high switching costs. In a low-rate world, that translated into a seductive kind of collateral: businesses that felt more stable than cyclical industrials, cleaner than consumer names, and easier to value on growth plus retention metrics.

But this is where the regime has changed.

Pressure is already building in parts of the market, with lenders tightening terms and becoming more selective. AI makes software economics more contestable. Slower growth, longer sales cycles, pricing pressure, and weaker exit multiples do not need to produce instant insolvency to make a highly levered capital structure feel much tighter than it did under the assumptions of 2021.

The Bridge to the Fault Line

The danger in private credit is not usually that fundamentals deteriorate on a Tuesday and the market collapses on a Wednesday. It is that the signs of deterioration surface first in public proxies (BDC discounts, asset manager equities, software valuations) while the private loan book remains comparatively still. The mark lags the market, and the refinancing test arrives later. By the time a quarterly valuation fully admits the problem, the question of whether a borrower is weak is stale, and the new question is whether the system can still roll the loan on terms that preserve the illusion of stability.

Private credit repackaged credit risk into a slower-moving, more bespoke, less transparent form. That was enormously attractive in its golden age. There is no question about whether that model ever worked. It clearly did. The issue is whether a market built to look steady can stay steady once one of its favourite borrower cohorts starts repricing faster than the loans written against it.

This Is No GFC Call

The right way to frame this section is not “private credit will be the next 2008.” That’s too lazy, and it misses the shape of the problem. The more useful framing is that private credit is finally being tested in a new regime, and one that it never really had to underwrite for at scale: weaker software valuations, stickier refinancing risk, more sceptical investors, and public proxies that now move faster than private marks. There may be pain, contagion, and some losses, but that is not the same thing as saying the asset class is about to detonate into a system-wide event.

That said, the fact that this is probably not a classic banking crisis does not make it any less important. What changes the tone in 2026 is that regulators no longer treat private credit as a niche corner of the market. The US Securities and Exchange Commission (SEC) is explicitly warning that opacity, valuation, transparency, and credit quality matter amid elevated redemptions and rising default projections.

“The SEC is closely monitoring both the lending gap that private credit has filled and the emerging pressures that it has experienced, including elevated redemption requests and rising default-rate projections.”6

More so, the Bank of England (BOE) has launched a system-wide stress test to understand how banks, insurers, and non-banks active in private markets behave in a downturn. The European Central Bank (ECB) has begun flagging private credit as a financial stability concern. And the Financial Conduct Authority (FCA) has already warned that private markets require stronger valuation governance, as much depends on judgment rather than continuous price discovery.

Whether an asset class built on infrequent marks and patient capital can absorb a shock is a far more interesting debate than simply saying the whole thing explodes.

The Market Is Already Speaking

If you want to know where the stress is showing up first, don’t begin with default data. Begin with the public wrappers. Listed BDCs recently traded at their deepest discounts to net asset value in more than five years, with the average ratio around 0.856 at the end of March.

At the same time, Moody’s has shifted its outlook on US BDCs to negative, citing redemption pressure, higher leverage and weakening access to funding markets. That is all before you get to flows. Direct lending fundraising fell to $10.7 billion in the first quarter, the weakest quarterly number in three years, while new money into private credit funds aimed at wealthy individuals fell 45% year on year.

The loans themselves don’t trade every hour, but the equity around them does. So when investors have concerns, the first real price discovery happens in the discount to book and share prices of listed managers.

There is also a structural reason that this matters more now than it would have a decade ago. Some of the largest managers have made retail and high-net-worth fundraising a central growth engine, and some estimates put retail assets at around a quarter to two-fifths. A market that was sold as institutional and relatively insulated from mood swings becomes more exposed to investor behaviour. That in itself is not automatically dangerous. But it does mean that sentiment can now tighten conditions for the asset class even before credit losses force the issue.

Why Software Is the Pressure Point

The critical fault line is software, not because every software company is suddenly broken, but because private credit ended up with more software exposure than much of the rest of the sub-investment-grade universe.

Lending by private credit funds to SaaS firms rose from almost $8 billion in 2015 to more than $500 billion by the end of 2025, equivalent to 19% of total direct loans (chart below). Even that may understate the true exposure, as classification can blur the picture. A software company selling into healthcare may be reported as healthcare exposure; one serving financial services may sit under financials. The result is that “software risk” is often larger than headline sector buckets suggest.

BDCs extended over 15% of their loans to SaaS firms in 2025, and firms with greater software exposure underperformed peers by around five percentage points last year. Analysis of more than 2,400 sponsor-backed middle-market borrowers shows software accounts for roughly 17% of borrowers and 22% of total debt exposure.

A concentration of that size was not irrational in the old regime because of the business model that software offered. But the same characteristics that made software look safe also encouraged aggressive underwriting. Much of direct lenders’ exposure to software originated at elevated valuations and leverage, and a meaningful slice consists of annual recurring revenue loans to companies with little or no positive free cash flow. Software-heavy deals have tended to cluster in the upper middle market, precisely where covenant protection and underwriting discipline have weakened the most.

The AI angle should also be kept in proportion. We do not buy into the interpretation that “AI kills software, therefore software kills private credit.” AI risk is likely to be diffuse and manageable overall, and most software-adjacent borrowers still have time and flexibility to adapt. But sponsor-backed borrowers with near-term maturities and structural exposure to disruption may come under real pressure. The risk falls in two channels: obsolescence, where customers build or buy cheaper functionality elsewhere, and margin compression, where incumbents have to spend more and price lower to defend their position.

This debate is no longer theoretical. In the week of publication, several of the largest private credit managers moved to reassure investors on this point. Ares, Blackstone and Blue Owl have begun running AI vulnerability reviews across their software books, using internal scorecards, external consultants, and refreshed underwriting work to separate potential beneficiaries from genuinely exposed borrowers. The message was deliberately calming (talk your book, so to speak). They state the risk is present, but most senior loans are presented as insulated, with only a limited share of exposures deemed high-risk. Blackstone said less than 5% of its flagship private credit fund investments faced AI headwinds, while Ares said 85% of its software-oriented investments were low risk and only 1% were high risk.

We would treat that as useful, but not conclusive. The exercise itself tells you the market has moved from abstract concern to active portfolio triage. The fact that managers are now producing AI scorecards is encouraging from a transparency perspective, but it also reinforces the broader point: software exposure is a meaningful risk. It is now something investors are actively trying to measure and stress-test.

Plumbing Matters More Than the Headline

The next fault line is liquidity structure. Semi-liquid private credit vehicles generally offer quarterly repurchases capped at about 5% of NAV, and those limits are now being tested. Gates are not evidence of collapse so much as a contractual mechanism to manage cash flow when the underlying assets are less liquid and must be held to maturity. Private-loan drawdowns in stress are more a function of wider spreads than sudden fundamental impairment, and portfolio cash flows through 2025 still ran above standard quarterly and annual redemption maximums. Redemption limits appear to have worked as designed by reducing the risk of fire-sale liquidation in recent months.

But it would be a mistake to stop there and declare everything fine. Once investors learn that liquidity is conditional rather than instinctively available, and if that change in expectations persists, the consequence is not necessarily an immediate run. The feed-through would more likely be slower fundraising, more cautious deployment, and a higher hurdle for new money. That is already evident in the drop in retail-oriented fundraising and in the broader scepticism towards non-traded BDCs.

How the Stress Travels

The most plausible stress path is mechanical rather than dramatic. It begins with weaker software equity values and more uncertainty around terminal value. That makes sponsors less willing to support marginal borrowers, makes EBITDA add-backs look more heroic, and raises doubts around whether old marks still deserve to be carried where they are. Investors then express that doubt in the places they can actually trade: listed BDCs, alternative asset managers, public software equities and the financing exposures of banks to private credit funds. This first part is already underway. It would be hard for anyone to argue against that.

JPMorgan Chase has already re-marked some loans to private credit funds with software exposure, and banks have become tighter and more selective in lending against the sector.

The next stage (sorry if this is not sexy or doomer enough for a Substack post) is not a wave of defaults. It is a worsening in the refinancing environment. As this year’s market has shown, private credit funds can cap redemptions and avoid forced liquidation. But a manager facing slower inflows, scarcer warehouse financing, and more cautious investors will also be less eager to extend and support every marginal software name on borrower-friendly terms. That is where the public and private feedback loop begins. Discounts in listed vehicles raise the cost of equity, wider bond spreads raise the cost of liabilities, banks re-mark or tighten facilities, and the marginal borrower discovers that the old assumption of endless maturity extension was a feature of the previous regime, not a permanent right.

A Banking Spillover Looks Limited But Real

This is also where the “not 2008” point needs to be held carefully rather than used as a comfort blanket. Bank commitments to private credit vehicles rose from around $8 billion in 2013 to about $95 billion by late 2024, and to roughly $322 billion when private equity and private credit vehicles are combined. Financial-stability implications appear limited so far, as exposures remain relatively small and the credit quality of bank loans is high. As we discussed in the previous section, banks and non-banks are not separate but interwoven, with risks transformed and repackaged rather than cleanly migrating outside the system.

Recent headlines support this middle-ground view. The Fed has asked major banks for details on their exposure to private credit firms, and the Treasury is consulting insurance regulators about fund-level leverage, ratings consistency, and liquidity. Several regional banks have now disclosed more than $230 billion in loans to non-bank financial institutions while insisting their books remain sound. Regulators can clearly see the transmission channels: bank credit lines to funds, leverage against loan portfolios, insurer demand for credit risk, and the possibility that tighter conditions in one part of the ecosystem reduce credit supply elsewhere.

So is the right conclusion “contained, therefore harmless”? Probably not. The answer is more “contained for now, but increasingly relevant.”

What to Actually Worry About

Our base case is not a sudden unravelling, although that would make for a catchy title. We’ll likely see a wider gap between good and bad underwriting. The era of uniformly strong, low-dispersion direct lending returns to an end, with outcomes increasingly driven by manager quality and sector selection. The primary transmission of AI risk is likely to be greater differentiation in returns rather than a wholesale ratings event (there will be software winners from AI, too).

The bear case is still easy to sketch, however. If AI pressure on software coincides with a more general slowdown, the weak cohort is obvious. In that world, BDC discounts stay wide, unsecured funding costs travel higher, and sponsors triage portfolios more aggressively. Today’s valuation problem becomes tomorrow’s refinancing problem. And none of that requires a banking panic, just enough borrowers to discover that the private market will not keep extending time on yesterday’s assumptions.

That, to us, is the real fault line. Private credit has not abolished the credit cycle. It has concentrated the lender group and shifted risk into vehicles that appear calm until they are forced to explain themselves. Software and AI matter because they accelerate the repricing of one of the asset class’s favourite borrower cohorts. Private credit is not the next crisis, but whether it can remain stable enough once the repricing reaches the refinancing calendar remains to be seen.

If you enjoyed this dive into the world of private credit and the stress test markets are currently facing, please leave a like to show your support. As always, comments and opinions are welcome. If you want to read more primers, you can check out our library here.

AP Research

The metal occupies a distinct position in global markets, both as a precious metal held for investment and as a critical industrial input consumed in manufacturing, particularly in solar, electronics, and electrification infrastructure. That dual role means silver responds not only to real rates and currency dynamics, as gold does, but also to fabrication cycles, inventory management, and commodity financing conditions.

However, the physical market tightened as years of underinvestment met rising demand from solar, electrification, and defence electronics. Financial flows turned pro-cyclical, ETF creations absorbed thousands of tonnes, lease rates spiked, and the curve flipped into backwardation, revealing how little truly free inventory there was.

Price strength through 2025 reflected these structural pressures. However, early this year, silver quickly became a speculative bet as availability declined across both the physical and financial markets, reinforcing its sensitivity to liquidity, positioning, and supply-chain rigidity.

Now, silver is trading less as a leveraged expression of gold and more as a hybrid asset shaped by industrial demand, balance-sheet capacity, and the mechanics of the bullion market.

In this primer, we’ll consider the following:

• Drivers of Price Action

• Market Structure

• Notable Historic Events

• Trading Strategies

• Market Participants

  Subscribe now

Key Drivers

Demand Dynamics

Silver’s demand profile continues to evolve in ways that structurally distinguish it from other precious metals. In 2024, industrial use accounted for approximately 58% of total silver demand, dwarfing jewellery and investment in many regions.

Within that industrial bucket, photovoltaic (PV) consumption alone absorbed roughly 170 million ounces, making solar the single largest end market, followed by electronics and electrical applications at about 140 million ounces combined.

In itself, the broader electrification trends, which include themes like power distribution infrastructure and advanced semiconductors, are a key factor in why we believe silver can keep moving higher in the medium term.

Supply Metrics

On the supply side, silver’s market dynamics reflect persistent tightness rather than surplus. The global silver market has now recorded five consecutive years of deficits, with the 2025 shortfall projected at about 118 million ounces, according to industry estimates.

Mine supply averages roughly 800-850 million ounces per year. Recycling adds a variable secondary stream.

Primary mine output is constrained by the fact that roughly 70–75% of silver is produced as a by-product of base metal mining. It has grown at a muted 1–2% per year, which is insufficient to offset rising consumption and again supports a conviction that silver is heading higher over the coming year.

Unlike primary commodities, higher prices do not quickly translate into new production because mine plans are driven by the economics of base metals rather than silver. That structural linkage leaves the supply side relatively inelastic, with incremental availability coming mainly from scrap flows. In practice, a significant portion of privately held silver (particularly heirloom holdings across parts of Asia) rarely re-enters the market, limiting the extent to which recycling can respond even during periods of elevated prices.

Liquidity Contraints

Liquidity and positioning act as the third driver. Silver futures are thinner than gold, ETF inventories are smaller, and physical availability is more constrained. As a result, speculative flows, CTA positioning, and options hedging exert disproportionate influence on short-term price dynamics.

This is why silver tends to overshoot both up and down. It is not just directionally sensitive. It is mechanically convex.

A good example illustrating this came in December last year. The month began with the market already tight after the October London funding shock (lease rates north of ~11% at the peak of the squeeze), and with most London vault inventory effectively unavailable to the spot market (end-September holdings were ~24,581 tonnes, remember ~83% was allocated).

Amid the thinner year-end liquidity, spot silver printed successive upside air pockets just before people started leaving for the holidays.

The critical point for shorts was that the physical market never really “relaxed” even when the price wobbled. Bullion market reporting still showed London lease rates around ~6% in early December, an extreme financing signal for a liquid precious metal and consistent with a scramble for borrowable ounces.

As momentum funds and dealer hedging joined the move, the squeeze phase migrated from financing spreads into outright price, culminating in the late-month vertical as silver hit $77.40/oz on 26 Dec and later cleared $80/oz before year-end.

In parallel, investor vehicles pulled incremental metal out of circulation: silver exchange-traded product inflows surpassed 4,000 tonnes, reinforcing the “available float” problem that makes short futures exposure fragile when delivery optionality suddenly becomes binding.

Correlations

Silver’s cross-asset correlation profile reflects its dual role as both a monetary and industrial metal. The strongest relationship remains with gold, where shared sensitivity to real rates and global liquidity conditions anchors silver firmly within the precious-metals macro complex.

The positive, but materially lower, correlation with copper and broad commodity indices (GSCI, CRB) captures silver’s partial exposure to global manufacturing and electrification cycles, particularly via electronics and solar demand.

More modest correlations with equities and crude oil reflect silver’s tendency to participate episodically in reflation and risk-on environments, but it is not a consistent proxy for growth or energy.

Finally, the negative correlation with the broad dollar speaks to the opportunity cost of holding an asset with yield versus one without. A higher interest-rate environment typically sees a bid for the greenback, with a wider carry differential against silver (and vice versa).

Market Structure

The global silver market mirrors gold in architecture but not in depth. Annual turnover across London and New York comfortably exceeds $5–7 trillion notional, yet the amount of metal that can be mobilised at short notice is a fraction of that implied liquidity.

Liquidity is split between the OTC market, coordinated through the LBMA, and exchange-traded futures, primarily on COMEX via the SI contract. Average daily volume on COMEX typically runs 60–80k contracts (equivalent to 300–400 million ounces), or more than one-third of annual mine supply changing hands each day. Open interest, however, represents claims on metal that are often 5–7x larger than registered deliverable stocks.

The London market dominates physical clearing, unallocated balances, and institutional custody. Roughly 85–90% of global spot transactions are estimated to price off the LBMA loco-London market, where bullion banks act as intermediaries between buyers. Pricing here reflects physical availability, lease rates, and balance-sheet conditions rather than futures sentiment alone.

The LBMA publishes vault holdings data each month. As of the January 2026 report, there were also 27,729 tonnes of silver (a 0.3% decrease on the previous month), valued at $92 billion, which equates to approximately 924,304 silver bars.

COMEX provides leverage, speculative access, and price discovery. It is where macro funds, CTAs, and producers express directional views, hedge exposure, and trade volatility. The contract is highly financialised: less than 3% of open interest normally proceeds to delivery, yet the threat of delivery disciplines the entire curve.

The two venues are linked through EFP mechanisms, forward curves, and bullion-bank arbitrage desks. But unlike gold, where visible stocks exceed 200,000 tonnes, silver’s accessible float is narrower.

ETFs such as SLV and SIVR play an outsized role in silver. Their custody holdings often represent 30–40% of identifiable above-ground investment stocks, compared with roughly 10–12% for gold ETFs. Creation or redemption cycles of just 1,000 tonnes (barely a few weeks of mine output) can therefore swing lease rates by several percentage points and invert spot-futures spreads.

Notable Market Episodes

Silver’s modern history has been punctuated by episodes of concentrated speculation and abrupt reversals, underscoring its sensitivity to leverage, liquidity, and retail participation. The most infamous example occurred in 1979–1980, when the Hunt Brothers, Texas oil magnates, attempted to corner the silver market by amassing large physical holdings alongside heavily leveraged futures positions. Their aggressive accumulation drove prices from roughly $6 per ounce to nearly $50 in less than a year. The rally ultimately proved unstable. As volatility intensified, COMEX raised margin requirements and imposed position limits, effectively tightening financial conditions within the market. The resulting forced liquidations triggered a violent collapse, with prices falling back below $10. The episode remains a defining lesson in the dangers of leverage in relatively thin markets, where liquidity can evaporate quickly once policy or exchange rules shift.

Three decades later, silver again surged during the 2011 QE2-era precious metals boom. Fears of dollar debasement, inflation overshoot, and unconventional monetary policy attracted both institutional and retail inflows into precious metals. Silver, with its higher volatility and smaller market size relative to gold, reacted with exaggerated momentum. Prices climbed from approximately $18 to nearly $50 within months, revisiting levels last seen during the Hunt episode. However, once inflation concerns subsided and global liquidity conditions stabilised, speculative positioning unwound just as rapidly. The subsequent collapse reinforced silver’s reputation as gold’s higher-beta counterpart, capable of dramatic upside during macro stress, but prone to sharp reversals when conviction fades.

More recently, in early 2021, silver briefly became the focus of a coordinated retail-driven campaign inspired by the broader “meme stock” phenomenon. Retail traders mobilised through social media attempted to engineer a so-called “silver squeeze,” directing flows into physically backed ETFs and encouraging collective buying pressure. The episode produced a short-lived price spike and record trading volumes in vehicles such as SLV. However, unlike in equities, where short interest can create mechanical squeeze dynamics, silver’s deep institutional liquidity and global OTC market absorbed the flows without triggering sustained physical bottlenecks. Prices quickly retraced as speculative enthusiasm waned, illustrating once again that while silver can respond violently to concentrated positioning, structural market depth ultimately constrains attempts to force prolonged dislocations.

2025/26 - Present

The January 2026 episode belongs in the same lineage as 1980 and 2011, but it was not driven by a new macro narrative. It was the moment when already-tight physical conditions collided with an overcrowded financial expression of that thesis.

By late 2025, the market had largely accepted the structural deficit story. What changed into year-end was positioning. Investors increasingly treated silver not as a slow-moving industrial theme but as a momentum trade built on genuine scarcity. ETF accumulation, derivatives exposure, and retail participation pulled incremental metal out of circulation while simultaneously increasing the volume of paper claims against it. The tradable float was shrinking as leverage was rising.

The final phase of the rally showed the classic characteristics of a late-cycle squeeze. Derivatives activity surged in the last week of January 2026, with exchange data showing exceptionally heavy futures and options turnover, consistent with concentrated speculative positioning. Prices accelerated vertically, culminating in an all-time high near $120.6/oz on January 29th.

What followed was not a reassessment of solar demand, mine supply, or long-term scarcity. It was a balance-sheet event.

On January 30th, silver fell roughly 25–31% in a single session, one of the largest daily declines since 1980, while ETF proxies recorded collapses of similar magnitude on extreme volume. The trigger was a rapid tightening of financial conditions inside the market itself. As volatility surged, CME raised margin requirements under a revised methodology, forcing leveraged participants to post collateral or reduce exposure.

In a market with limited depth, that shift flipped the reflexivity. The same leverage that had amplified the rally now accelerated liquidation. Selling pressure became mechanical rather than discretionary, producing a non-linear air pocket characteristic of silver’s most violent historical reversals.

Importantly, the structural backdrop did not disappear. Data continued to show persistent deficits and strong industrial demand even as prices corrected. What the episode demonstrated instead was how quickly silver can transition from a fundamentals-led bull market to a liquidity-driven unwind once positioning saturates.

If the 2025 move was about scarcity, the January 2026 move was leverage discovering that scarcity is not the same thing as liquidity.

We had the following film play out, but in the metals market…

Trading Strategies

Spot vs ETF Exposure

If the objective is long-term, unlevered exposure, spot remains the most direct route. An investor can access this primarily through the London OTC market.

Trading spot silver typically involves transacting bilaterally with a bank or bullion dealer, with settlement on a T+2 basis. On settlement, cash is exchanged for a silver position credited to a metal account. That account can be held on an unallocated basis, representing a general claim on metal within the dealer’s vaulting system, or on an allocated basis, where specific LBMA-approved bars are set aside in the investor’s name, with serial numbers recorded and custody fees charged.

From there, the position can be held outright, leased into the market, rolled into forwards or swaps, or converted into physical delivery. For participants with access, this is the cleanest way to engage directly with the underlying bullion market.

For many investors, however, ETFs such as SLV provide a more accessible alternative. These trade on equity exchanges, clear through standard securities infrastructure, and offer intraday liquidity without the operational complexity of bullion accounts. While ETFs lack some of the flexibility of the OTC market, they are often the most practical solution for smaller ticket sizes or for accounts without access to London metals desks.

Spot XAG/USD can also be attractive where available because it trades like a currency pair, offering near-continuous liquidity and tight execution. That said, there are clear reasons why spot or ETFs may not be appropriate:

• Full notional must be funded, limiting capital efficiency

• Shorting is operationally and economically more complex

• The exposure may need to be future-dated or aligned to another portfolio hedge

In those cases, an investor typically moves into derivatives, such as futures and forwards.

Futures vs Forwards

Silver exposure can be expressed through OTC forwards or exchange-traded COMEX futures. Both introduce leverage, simplify short positioning, and allow exposures to be tailored to specific horizons.

Forwards are negotiated bilaterally, typically out of London, and are fully customisable by size and maturity. Crucially, they embed physical-market dynamics, including lease rates and inventory conditions. When the trade thesis is tied to physical tightness or carry, this matters.

A live example is the recent backwardation in London silver, where spot has traded above forward prices. Silver normally trades in contango due to storage and financing costs. Backwardation reflects near-term scarcity.

In such an environment, a trader does not need a directional view on silver to express a thesis. Selling spot and buying a six-month forward is a way to position for a normalisation of the curve as tightness eases and lease rates fall. The P&L is driven by curve dynamics rather than outright price. That said, forwards are not guaranteed to converge smoothly, and access remains largely institutional.

For most tactical applications, COMEX futures are the more practical tool. They are standardised, centrally cleared, and highly liquid across benchmark maturities. Margining allows for capital-efficient positioning, and the contract suite now supports much finer sizing.

The introduction of the CME 100-ounce silver futures contract has been particularly important, materially lowering the notional barrier to entry versus the legacy 5,000-ounce contract and improving execution flexibility for active traders.

Options Strategies

There are periods when neither spot nor linear derivatives are the right tool. When the objective is defined risk, volatility exposure, or event-driven positioning, we move into options.

Silver options are where the market’s character is most visible. Implied one-month volatility recently broke to multi-decade highs, reflecting expectations of large and potentially disorderly price swings.

At those times, price-agnostic structures often dominate. Calls and puts can be used to express long or short volatility views, while spreads and convex structures can be built to shape asymmetry. Around discrete catalysts such as central bank meetings, macro releases, or geopolitical risk, straddles and strangles allow positioning for movement rather than direction.

For more advanced implementations, skew becomes a tradable variable. Relative pricing between calls and puts often reflects embedded positioning or hedging demand. In our experience, distortions in skew can at times be isolated and traded independently of outright direction.

When silver is being treated primarily as a volatility instrument rather than a price instrument, options tend to dominate.

Relative-Value Structures

There are also periods when an investor may not want outright silver exposure at all. Some of the most consistent strategies in the precious-metals space have been relative-value trades.

The gold–silver ratio remains the most common framework. Rather than forecasting absolute levels, the trade is structured around the relationship between monetary and industrial metals. Extreme readings often coincide with macro dislocations, positioning imbalances, or shifts in demand regimes.

A typical implementation is via paired COMEX futures positions: going long silver and short gold when the ratio is elevated and silver appears undervalued, and reversing the structure when the ratio compresses. This approach neutralises much of the shared dollar and real-rate sensitivity, allowing returns to be driven primarily by relative repricing.

Silver Equities

Primary silver miners and streaming companies provide a levered and operationally sensitive way to express silver views.

The benefit of trading single-name equities is the operational leverage gained from higher commodity prices. The company benefits from the elevated selling price almost immediately, while incremental extraction costs increase at nowhere near the same pace in the short run.

A good example is Pan American Silver Corp (PAAS), which is owned in our Master Fund and is up 180% over the past year.

Market Participants

Primary Producers

Mining companies represent the natural structural shorts in silver. Their objective is revenue stability rather than directional price exposure, and this distinction shapes how they interact with the market.

Because a large share of global silver output is produced as a byproduct of other metals, hedging decisions are often balance-sheet driven rather than price-driven.

Producers tend to increase forward sales when prices rise, when leverage increases, or when lenders demand risk management discipline during capex cycles. This flow profile makes producer activity procyclical. They sell strength and reduce hedges into weakness, which rarely determines trend direction but frequently influences trend velocity by supplying liquidity into rallies and withdrawing it during drawdowns.

Industrial Users/Fabricators

Industrial consumers form the structural long base of the silver market because they rely on the metal as a production input.

Their participation is motivated by cost control rather than return generation, and their hedging behaviour reflects procurement discipline rather than speculative intent.

Fabricators typically layer hedges over time and increase activity after price rallies, when procurement risk becomes more visible to management. Conversely, they tend to step back during declines and rely on spot purchasing.

Industrial demand therefore anchors long-run price floors while remaining largely irrelevant for short-term price discovery.

Bullion Banks/Cap Markets Desks

Bullion banks function as the core transmission mechanism of the silver market. They intermediate nearly all OTC flow, warehouse risk, structure derivatives, and arbitrage price differences across spot, forwards, futures, and leasing markets.

Dealer balance sheet capacity is a first-order determinant of liquidity. When risk appetite is strong and balance sheets are elastic, spreads tighten and markets absorb flow efficiently. When constraints bind, liquidity deteriorates quickly and price movements can become discontinuous.

Lease rates in particular provide insight into physical tightness and dealer funding stress, making them an underutilised signal for institutional investors monitoring market conditions.

Fund/Asset Managers

Institutional capital accesses silver primarily through futures, ETFs, and structured products, and it is this cohort that most often determines the marginal price.

Unlike producers or industrial users, these participants are explicitly return-seeking and frequently momentum-sensitive. Macro funds and commodity index vehicles all interact with the market through rule-based or macro-driven positioning frameworks.

Because silver’s market depth is thinner than gold’s, reallocations by large funds can produce outsized price reactions relative to flow size. ETF flows are especially powerful because physically backed vehicles must acquire or sell metal to match share creation or redemption. As a result, institutional positioning frequently amplifies trends rather than dampening them.

Retail Investors

Silver has historically attracted disproportionate retail participation compared with other metals. Its lower nominal price per ounce creates a perception of accessibility that draws individual investors into coins, small bars, leveraged derivatives, and short-dated options.

Retail participation tends to surge during periods of macro anxiety, currency debasement narratives, or viral social media attention. While individually small, retail traders can collectively influence price during liquidity-thin periods, particularly in derivatives markets where leverage magnifies impact.

Sovereigns/Central Banks

Central banks play only a minor direct role in silver markets, in contrast to their structural importance in gold. Official holdings are limited, and transactions are infrequent, which removes a stabilising demand base that might otherwise dampen cycles.

Conclusion

Silver is not one market. It is a stack of markets layered on top of each other (physical, financial, industrial, monetary), each governed by different participants, constraints, and reflex loops.

That structural complexity is why silver repeatedly confounds linear frameworks. Analysts who treat it as a simple commodity underestimate its financing channel. Those who treat it as a monetary metal miss its fabrication cycle. And those who view it purely through the lens of positioning risk, overlooking the physical plumbing that ultimately enforces delivery discipline.

If you enjoyed this Primer, please drop a like or comment below. If you want to read more Primers, check out the library here.

AP

Gold broke higher not as a speculative trade, but as a balance-sheet response to a shifting macro regime.

The precious metal sits at an unusual intersection of narrative and necessity. It is a currency without a central bank, a monetary asset without an issuer, and one of the few instruments that exists both inside and outside the financial system at the same time.

Unlike bonds, it carries no credit risk. Unlike currencies, it does not depend on policy credibility. Its value rises when confidence in monetary and fiscal frameworks begins to fray.

Investor demand in 2025 reflected deep concerns around real rates, fiscal sustainability, reserve diversification, and the long-term purchasing power of fiat money. As the dollar weakened and long-end bonds lost their defensive appeal, gold reasserted itself as a neutral reserve asset—one held not for yield, but for safehaven.

Subscribe now

In this primer, we’ll consider the following:

• Drivers of Price Action

• Market Structure

• Notable Historic Events

• Unique Market Dynamics

• Trading Strategies

• Market Participants

Key Drivers

Gold price action is primarily driven by the opportunity cost of holding a non-yielding asset, which makes real interest rates (typically proxied by the 10-year TIPS yield) the core macro variable to watch: lower real yields reduce the carry disadvantage and support higher gold prices, while rising real yields exert downward pressure.

The US dollar is the secondary anchor. Given gold’s denomination and its role as a reserve alternative, dollar weakness generally corresponds with inflows into gold as global purchasing power improves and FX-hedged returns rise. Global liquidity conditions also play a significant role: periods of monetary expansion, QE, or abundant reserves tend to lower discount rates and increase portfolio hedging demand, while liquidity withdrawal or tightening cycles create headwinds.

Beyond macro rates and currency factors, central bank reserve-management flows have become increasingly influential, particularly among emerging market reserve managers diversifying away from USD exposure. This source of demand is strategic, price-insensitive, and structurally supportive.

Meanwhile, ETF flows, futures positioning, and CTA trend signals drive shorter-term moves, often amplifying directional momentum around data releases or macro inflexion points.

Finally, geopolitical risk and systemic stress function as convexity triggers rather than steady-state drivers: gold does not rise simply because uncertainty increases, but it tends to outperform sharply when confidence in policy, institutions, or cross-border capital mobility becomes impaired.

Market Structure

The global gold market is defined by a two-tier structure: a deep, liquid OTC market centred on the London Bullion Market Association (LBMA), and a highly standardised futures market anchored at COMEX in New York.

The London market is where the majority of physical and unallocated gold trading occurs, with bullion banks, central banks, refiners, and sovereign wealth funds transacting primarily through forwards, swaps, and spot transfers settled via London Good Delivery bars. This OTC system functions as the core pricing venue and liquidity pool.

COMEX, by contrast, provides financial leverage and price discovery through the GC futures contract, which is widely used by macro hedge funds, CTAs, and producers for hedging and speculative positioning.

Although futures volumes often exceed visible physical flows, most futures contracts are closed out or rolled rather than delivered, with the EFP (Exchange for Physical) mechanism linking COMEX futures pricing back into London spot liquidity.

Complementing these institutional venues are exchange-traded products such as GLD and IAU, which provide scalable retail and asset management access. Inflows and outflows in these vehicles influence custody holdings in LBMA vaults and therefore matter at the margin for spot.

Meanwhile, central banks now represent a structurally meaningful bilateral flow driver, accumulating physical reserves outside public markets and reinforcing the physical floor under the asset.

Notable Market Episodes

Gold’s modern trading history begins in earnest with the collapse of the Bretton Woods system in 1971, when the United States suspended dollar convertibility into gold. This decision transformed gold from a fixed-price reserve anchor into a free-floating monetary asset. From that point onward, gold began trading not as a static store of value, but as a dynamic barometer of monetary credibility, policy discipline, and global liquidity conditions.

The 1970s marked gold’s first major test in this new regime. A combination of expansionary fiscal policy, repeated oil shocks, and persistently negative real interest rates created an environment in which confidence in fiat money deteriorated steadily. Gold responded with a structural repricing, rising sharply throughout the decade and culminating in a dramatic peak in 1980. Over this period, gold prices increased by roughly 200%, reflecting not just inflation fears but a broader breakdown in monetary anchor points.

In contrast, the 1990s represented a prolonged period of disinterest and decline. With inflation subdued, real rates positive, and financial globalisation accelerating, gold came to be viewed by Western policymakers as a non-productive and increasingly obsolete reserve asset. Central banks in Europe and elsewhere became net sellers, contributing to a secular downtrend that saw gold prices fall to roughly $250 per ounce by 1999. That same year, the Central Bank Gold Agreement (CBGA) was introduced to coordinate and cap official sector sales, helping to stabilise expectations around reserve flows and establish a durable price floor after years of erosion.

Gold’s role was once again redefined in the aftermath of the global financial crisis. Between 2008 and 2011, collapsing real yields, systemic financial stress, and the introduction of large-scale quantitative easing programs restored gold’s relevance as a hedge against monetary regime uncertainty. The metal rallied strongly over this period, reflecting both fears of currency debasement and the repricing of long-term confidence in central bank balance sheets.

More recently, the post-2020 environment has reinforced gold’s appeal through a different channel. Pandemic-era fiscal expansion, rising public debt burdens, deglobalization pressures, and increased geopolitical fragmentation have all contributed to renewed central bank accumulation of gold reserves, particularly among emerging market economies. Unlike previous cycles, this demand has persisted even in periods where real yields remained positive, underscoring that gold’s support has increasingly been driven by concerns around policy credibility, reserve diversification, and the long-term stability of the monetary order rather than by inflation alone.

A Different Way to Think About Supply and Demand

Gold does not behave like most commodities because it is not consumed. Nearly all the gold ever mined still exists, sitting in vaults, central bank reserves, or household balance sheets. The global above-ground stock is estimated at roughly 220,000 tonnes, while annual mine production adds just over 3,000–3,300 tonnes per year, a little more than 1% of total stock. This makes gold fundamentally a stock-driven market rather than a flow-driven one. Traditional supply–demand frameworks, which focus on balancing production and consumption, are therefore structurally ill-suited to explaining gold price dynamics.

Because annual mine supply is both small relative to existing stock and largely insensitive to price, gold does not clear through marginal production decisions. Miners cannot quickly increase output when prices rise, nor do they meaningfully curtail supply when prices fall. New projects take years or decades to develop, ore grades continue to decline, and most mines operate close to engineered capacity regardless of spot prices. As a result, changes in mine supply explain very little of gold’s short-term price behaviour. What matters instead is how much of the existing stock is available to trade.

Gold prices are therefore set by changes in ownership. When gold is bought, it must be sold by someone else, and the price adjusts until the identity of the marginal holder changes. In this sense, the gold market clears not through flows of new supply, but through the willingness of existing holders to part with their gold. The key question for price formation is not how much gold is produced, but who is more willing to hold it and what it takes to persuade someone else to let go.

This ownership dynamic creates a clear distinction between two broad types of buyers. Conviction buyers—central banks, physically backed ETFs, and macro or speculative investors—allocate to gold based on macroeconomic, financial, or geopolitical theses. Their demand is largely price-insensitive. When they want gold, they buy it regardless of level.

Opportunistic buyers, by contrast, are primarily households in emerging markets, where gold often functions as a form of savings. These buyers are price-sensitive, stepping in when prices fall and slowing purchases when prices rise. They rarely sell on net.

As a result, conviction buyers determine the direction of prices, while opportunistic buyers influence the amplitude of moves by providing a floor on the way down and resistance on the way up.

Empirically, this distinction matters. A report from Goldman Sachs on gold showed that net purchases by conviction buyers explain roughly 70% of monthly gold price variation. As a rule of thumb, every 100 tonnes of net conviction buying is associated with approximately a 1.7% increase in the gold price.

This relationship exists because the amount of gold that is actually available for sale at any point in time is small relative to the total stock, much of which is effectively locked away. Around 17% of above-ground gold sits in central bank reserves, roughly 22% is held in investment vaults, and a large share of the remainder is held as jewellery in emerging markets, where it is rarely mobilised for sale.

This structure explains why the traditional commodity adage that “high prices cure high prices” does not apply to gold. Rising prices do not unlock large volumes of new supply, nor do they induce widespread selling from opportunistic holders. When conviction buyers are net buyers, prices can rise sharply and persistently because there is no meaningful supply response to arrest the move. Gold prices only fall decisively when conviction itself breaks, and those same buyers step back.

At the most basic level, the market clears when conviction and opportunistic buying together absorb mine supply and any sales from existing holders. In practice, because mine supply is stable and opportunistic buyers rarely sell, most adjustments occur through conviction holders trading among themselves. This is why gold can remain supported despite positive real yields, fall during periods of elevated inflation, or rise even when ETF flows are negative, provided central banks or other conviction buyers are accumulating.

The practical implication is that gold is not a market where balance tables or production forecasts provide much insight. Understanding price action requires tracking who the marginal buyer is, whether conviction demand is expanding or contracting, and how much gold is actually available to be prised loose. In gold, price is not set by how much is produced, but by how much conviction it takes to change hands.

Trading Strategies

Gold offers a wide menu of trade expressions that extend well beyond outright spot or futures exposure. Most strategies are ultimately about isolating which transmission channel an investor wants to target.

Real Rates Expression

The most fundamental gold trade is a view on real interest rates. Gold has a persistent negative correlation with US real yields, particularly at the long end, making it an efficient way to express expectations around easing financial conditions, policy pivots, or declining term premia.

This can be implemented through:

• Long gold futures or XAU/USD against short real rates exposure (e.g. TIPS)

• Relative trades versus long-end Treasuries when bonds lose their defensive properties

Gold often outperforms bonds when real yields fall for the “wrong” reasons. We’re talking about fiscal dominance, volatility in inflation expectations, or credibility concerns, rather than clean disinflation.

Curve and Carry Trades

While gold is typically in contango due to storage and financing costs, distortions can emerge when lease rates, USD funding conditions, or physical tightness move out of alignment.

The current (and typical) curve looks like this:

In contango environments, investors can implement carry trades by holding physical or allocated gold while shorting futures further out on the curve. In rarer backwardation episodes (usually driven by temporary physical dislocations) spot can trade higher than futures, creating inventory-replacement opportunities.

Typically, gold futures trade in parallel with gold forward contracts (XAU/USD), but there are periods when dislocations occur, creating arbitrage opportunities. For example, during 2008/2009, when the financial crisis was causing chaos around the world, there were differences in pricing, illustrated below.

Returns in these trades are driven by financing spreads and curve shape, not by outright price direction, making them attractive for relative-value or balance-sheet-constrained investors.

Volatility and Options Structures

Gold options provide a clean way to express convex views on macro stress, regime shifts, or tail risks. Demand for downside protection and upside convexity often shifts asymmetrically depending on whether gold is being used as a hedge or a speculative instrument.

Common strategies include:

• Long volatility during periods of compressed implieds despite elevated macro uncertainty. Reviewing the historical ATM volatility is the easy part. Deciding if it’s cheap enough to buy is harder…

  

• Skew trades occur when call demand from hedgers or reserve managers distorts pricing

• Long-dated optionality captures regime risk rather than short-term data outcomes

Gold volatility tends to reprice sharply around policy inflexion points, debt sustainability debates, or currency regime stress.

ETF Flow and Physical Dislocation Trades

Physically backed ETFs introduce an additional transmission mechanism into gold pricing. Large creation or redemption cycles can temporarily push ETF prices away from underlying spot or futures markets due to settlement and inventory mechanics.

When dislocations arise, relative trades can be constructed by:

• Buying spot or futures and shorting ETFs during redemption pressure

• Buying ETFs and shorting futures when creation demand outpaces physical sourcing

These opportunities are episodic and capacity-constrained but can be attractive for well-capitalised arb desks.

Gold Equities as a Levered Expression

Gold equities provide an alternative way to express gold views, but with important caveats. Unlike gold itself, equities embed operational risk, cost inflation, jurisdictional exposure, and capital allocation decisions.

That said, correlation analysis shows meaningful dispersion in how closely different gold equities track the underlying gold price. Companies such as Gold Fields, Agnico Eagle, and Harmony Gold exhibit relatively high sensitivity to gold price movements, making them more suitable for directional gold exposure.

By contrast, firms like Barrick Mining show lower correlation due to diversification into copper and other assets, reducing their purity as gold proxies. Streaming and royalty companies such as Franco-Nevada offer gold exposure with lower operational risk but often trade more like long-duration financial assets, making them sensitive to discount rates as well as gold prices

Single-name positioning can therefore be used to express higher beta to gold through operationally geared miners, as well as having trade dispersion between “pure play” gold exposure and diversified balance sheets.

Market Participants

The gold market is shaped by a relatively small but highly specialised group of participants whose motivations span reserve management, balance-sheet intermediation, portfolio hedging, and speculative positioning. Unlike industrial commodities, end-use demand plays a limited role in price discovery. Instead, gold prices are set at the margin by institutions with the balance sheet capacity to accumulate, warehouse, or mobilise large stocks of metal.

Central Banks and Sovereign Institutions

Central banks are the largest and most price-insensitive buyers of gold, accumulating reserves for diversification, credibility management, and geopolitical risk mitigation.

Their activity (largely conducted via the LBMA) provides a structural floor for gold demand. Sovereign wealth funds and state investment agencies play a similar role, often allocating through physical holdings or OTC swaps. These participants have multi-year horizons and minimal sensitivity to short-term volatility.

Bullion Banks and Dealers

Global bullion banks (like JPM and HSBC) are the core liquidity providers. They intermediate between miners, refiners, ETFs, industrial users, and macro funds. Their books span physical inventory, unallocated accounts, forwards, swaps, and exchange-traded futures.

They manage complex carry books tied to USD funding conditions, lease rates, and client hedging flows. Bullion banks also maintain the arbitrage link between LBMA spot and COMEX futures, anchoring global price coherence.

Miners and Producers

Gold producers participate in the market primarily as risk managers rather than price setters. They hedge future production using forwards, swaps, and futures to stabilise cash flows and lock in margins. This hedging activity can generate persistent selling pressure in longer-dated maturities, particularly during sustained price rallies, but it has limited influence on spot prices.

Because gold mining supply is price-inelastic and planned years in advance, producer behaviour rarely drives short-term price dynamics. Instead, miners function as predictable, slow-moving counterparties whose activity matters more for curve shape than for outright price direction.

ETFs, ETCs, and Physically Backed Vehicles

Large physically backed ETFs create meaningful, price-sensitive investment flows. They serve as the primary access point for asset managers who cannot hold bullion directly. Inflows and outflows mechanically adjust physical demand through creation/redemption processes, making ETF flows a crucial indicator of investment sentiment. Europe’s ETC market plays a comparable role for institutional allocators outside the US.

Hedge Funds, CTAs, and Macro Traders

Speculative capital (largely via COMEX) drives short-term price discovery. Macro funds express views on real rates, USD liquidity, and risk appetite via gold futures or XAU/USD.

CTAs and trend-following systems amplify momentum, particularly in gold and silver. Relative-value specialists trade cross-metal spreads such as gold/silver or platinum/palladium to capture substitution cycles and curve dislocations. These players bring liquidity but also increase the market’s reflexivity.

Retail Investors and Physical Buyers

Retail participation in the gold market is geographically concentrated and highly cyclical. In emerging markets (particularly India and China) physical gold ownership serves as a form of household savings rather than discretionary consumption. This demand is price-sensitive, rising sharply during pullbacks and slowing during rallies, and it rarely turns into sustained net selling.

In developed markets, retail investors primarily access gold through coins, bars, and ETFs, with participation spiking during periods of macro stress or heightened volatility. While retail flows can overwhelm short-term liquidity during extreme episodes, they generally act as stabilisers rather than directional drivers.

Conclusion

Structurally, gold is a market defined by ownership rather than production. Supply is slow, predictable, and largely irrelevant at the margin. Price is set by conviction: by central banks, institutional investors, and macro allocators willing to warehouse trust when other assets demand it. Opportunistic demand smooths the path, but it is conviction that determines direction.

Gold is not a market to be analysed through balance tables or short-term narratives. It is a market where understanding the marginal buyer matters more than forecasting mine output, and where positioning, liquidity, and credibility interact in non-linear ways. Whether expressed through spot, futures, options, ETFs, or equities, gold remains one of the cleanest ways to engage with regime risk.

In an environment defined by rising debt, political constraints on policy, and an increasingly fragmented global system, gold’s role is unlikely to diminish. It may not always outperform. It may spend long periods consolidating. But when trust becomes scarce, gold has a habit of reminding markets why it still matters.

If you enjoyed this Primer, please drop a like or comment below. If you want to read more Primers, check out the library here.

AP

Related companies are selling record amounts in corporate bonds to fund the industrial-scale build-out of AI. Earlier this week, Meta sold around $30 billion of unsecured senior notes, the largest single high-grade tech bond sale of the year. Meanwhile, Amazon returned to the US debt market for the first time in three years, raising roughly $15 billion to underwrite its next wave of AI-driven cloud and logistics investments. Alphabet recently tapped both US and European credit markets for about $25 billion to fund the expansion of AI and cloud infrastructure worldwide.

Stack it all up, and the picture gets even louder. Since the start of September, Big Tech issuance alone has accounted for $90 billion1 in investment-grade debt issuance. For reference, this is more than double the annual run rate seen during the prior decade. Per Morgan Stanley, tech firms are expected to tap debt markets for as much as $1.5 trillion by 2028.

For bond investors, that means something new. Buying corporate debt provides exposure not to pipelines or power stations, but to data centres, AI hardware, cloud grids, and sprawling compute-intensive infrastructure. The “risk-free” halo around Big Tech credit is dimming just enough for spreads to wobble, and for investors to start asking the tough questions about returns on AI capex, power demand, and the runway for monetisation.

With that backdrop in place, it’s time to return to our Global Primer Series and strip corporate bonds back to basics.

When a business wants to expand, acquire, or simply roll its existing debt, it turns to the credit market. In exchange for capital, investors get a promise of periodic coupon payments and the return of principal at maturity.

But that promise carries risk. Unlike government bonds, corporates embed the possibility that the issuer could stumble or fail outright. That’s why corporate bonds don’t just trade at the risk-free rate. They trade at a spread.

This credit spread is the heartbeat of the market. It’s the price investors demand for taking on default and liquidity risk. And inside that spread lives the story of a company’s balance sheet, its competitive position, the cycle, investor sentiment, and, when things get shaky, fear.

Understanding corporate bonds is essentially about understanding credit spreads: where they are, why they move, and what they reveal about risk appetite and the economic outlook.

In this primer, we’ll run through:

- Structure and types
- How to price
- Understanding credit risk
- Market structure
- Trading and hedging strategies
- Notable events and current market dynamics

Subscribe now

Structure and Types

Corporate bonds vary widely across ratings, maturity, and structure. At issuance, each bond is given a rating by one of the major ratings agencies:

The grading is split into two. An investment-grade (IG) status is for any bond rated BBB–/Baa3 or higher. These are from stable issuers with lower yields, benchmarked against indices such as the Bloomberg US Corporate Bond Index.

In contrast, high-yield (HY) is for ratings below BBB–/Baa3. Also known as “junk bonds,” they offer higher coupons, higher default risk, and trade more like equities in stressed conditions.

Fallen angels, rising stars, and split ratings:

A fallen angel is a formerly investment-grade issuer whose credit quality has deteriorated enough to be downgraded into high yield. A rising star is the mirror image: a company upgraded from speculative grade into investment grade as its fundamentals improve. Because credit ratings involve judgment, agencies don’t always align, an occurrence known as a split rating.

Each of these situations can create opportunities for investors willing to take on additional risk, as the associated rating volatility and index-driven flows can offer pockets of extra yield.

Alternative Structures

Floating Rate Notes (FRNs) pay coupons linked to a short-term benchmark, such as SOFR, and reset periodically at the reference rate plus a fixed spread. This structure minimises duration risk. When rates rise, coupons adjust higher, keeping prices stable. Issuers use FRNs when they expect rates to fall or want flexibility in refinancing; investors use them to earn credit spread without taking directional rate risk. In hiking cycles, FRNs are popular among money market funds and insurers seeking yield pickup over cash, while behaving more like credit instruments than bonds.

Callable Bonds embed an option for the issuer to redeem the debt early, usually once rates have fallen or credit has improved. They carry negative convexity—prices rise less when yields fall because the bond may be called away—and therefore offer higher coupons to compensate. Callable structures suit issuers in volatile rate environments and investors who are comfortable trading yield for optionality.

Convertible Bonds flip the equation, giving investors the right to convert debt into equity at a preset price. They combine coupon income and principal protection with potential equity upside, effectively a bond plus a call option on the stock. Issuers benefit from lower coupons; investors gain asymmetric exposure to credit and equity risk. Convertibles perform best when volatility is high and growth optimism is returning.

Puttable Bonds give the investor, not the issuer, the option to sell the bond back to the issuer at par on specified dates before maturity. They’re essentially the mirror image of callable bonds. The put feature protects the holder against rising interest rates or credit deterioration. So if yields spike, spreads blow out, or the issuer’s fundamentals weaken, the investor can “put” the bond back and reinvest at higher prevailing rates. Because this embedded option is valuable to investors, puttable bonds typically offer lower coupons than comparable straight bonds.

Structural Features

Sinking fund provisions require the issuer to retire a portion of the bond’s principal on a scheduled basis before maturity. These amortisation payments reduce the amount outstanding over time, lowering refinancing risk and providing bondholders with incremental credit protection. While it softens default risk, a sinking fund can also introduce reinvestment risk, since investors receive principal back earlier than a bullet maturity.

Make-whole calls let issuers redeem bonds early, but only by compensating investors for the present value of foregone coupon payments, typically using a Treasury benchmark plus a contractual spread. Because the make-whole premium is designed to leave the investor economically indifferent, these calls rarely get exercised unless the issuer faces significant strategic or financial incentives. Bonds with make-whole calls tend to trade more like non-callable paper, with investors treating the provision as a modest backstop rather than a meaningful source of call risk.

Refunding protection restricts an issuer from calling a bond solely to refinance it with cheaper debt. This prevents issuers from opportunistically stripping investors of above-market coupons in falling-rate environments. Depending on the indenture, refunding protection can be partial or full, and can expire after a set period. It’s one of the more investor-friendly features in markets where callable structures are common.

The capital-structure position of the bond owned is important. Seniority determines where a bond sits in the payout waterfall during liquidation or restructuring. Senior secured is at the top, followed by senior unsecured, subordinated, and junior debt. Collateral provides an additional layer of protection when pledged, tying the bond to specific assets or cash flows. Higher seniority and collateralisation generally translate to lower coupons, tighter spreads, and higher recovery rates in distress. For investors, understanding exactly where a credit sits in the stack is crucial, since it often drives realised performance far more than yield at issuance.

Pricing Corporate Bonds

Corporate bonds contain several key pieces of information that investors will note down before deciding what action they want to take. For this breakdown, we provide as an example a JPMorgan USD bond that was issued in 2008 and matures in 2038:

Let’s run through some of the basics…

From there, you can look at the current price in order to work out the yield:

Given the bond is trading above par at 114.4, the yield is lower than the initial coupon (4.86% vs 6.40%).

Credit Risk, Default, and Curves

Credit Spreads

The market typically prices corporate bonds with higher credit risk than the comparable ‘risk-free rate’, often using the sovereign bond with the same maturity.

At issuance, this spread is noted. As in the case with the JPM bond, the added yield investors were compensated for in taking on the JPM bond instead of the corresponding Treasury was 195bps. This is known as the nominal spread.

Two other spreads worth noting down are:

Z-spread

The Z-spread (short for zero-volatility spread) is the constant spread added to the entire Treasury yield curve that equates the present value of a bond’s cash flows to its market price. In layman’s terms, the Z-spread tells you how much extra yield you’re earning over risk-free Treasuries if the bond’s cash flows are fixed and guaranteed to occur as scheduled.

It’s a cleaner, curve-adjusted way to measure credit risk than the simpler “nominal spread” (the yield difference versus a single Treasury).

OAS (Option-Adjusted Spread)

Many corporate and structured bonds have embedded options, which we discussed earlier. That’s where the OAS comes in.

The OAS takes the Z-spread and subtracts the value of any embedded options. In other words, it isolates the pure credit and liquidity premium after accounting for optionality.

Default & Recovery Pricing

Credit risk is the probability of loss from an issuer failing to meet obligations. The two key dimensions are:

• Default Probability: Often implied from credit spreads or CDS levels. For example, with JPMorgan, a trader could take a look at the 5-year CDS pricing.

  

  The CDS is at just over 45bps, meaning it costs approximately $45,000 per year to insure $10 million of JPMorgan debt against default for the next five years. The CDS figure can be converted using a rough rule of thumb to estimate the expected loss over this time period.

• Recovery Rate: This is the portion recovered after default (typically 40% for unsecured senior bonds).

Expected loss = Default Probability × (1 – Recovery Rate)

In the example we’re examining, assuming a 40% recovery rate, a 45bps spread implies roughly a 0.75% cumulative 5-year default probability for JPM, or approximately 0.15% per year.

In practice, credit investors also price liquidity risk, downgrade risk, and systemic correlation when assessing creditworthiness.

The Credit Curve

Just as Treasuries have a yield curve, corporates have a credit curve showing spreads across maturities.

In most conditions, the credit curve is upward-sloping, with longer-dated bonds having wider spreads than shorter-dated ones. Anyone who has studied markets knows that lending money for 10 years carries more uncertainty than lending for two years. Investors demand a higher premium to take on that duration and credit risk.

The curves can be plotted at a credit quality level or at a company-specific level. For example, the following shows the US IG Corporate curve, along with the JPMorgan one.

Risks Besides Credit

Although the market chooses to focus on credit as the key lens to view risk through, there are other risks to be aware of when it comes to trading corporate bonds.

Interest-rate risk is the big macro exposure. When yields rise, bond prices fall, and long-duration corporates get hit hardest. Liquidity risk comes next, especially in stressed markets where even investment-grade bonds can gap wider if dealers step back and balance-sheet capacity thins. Reinvestment and call risk show up when issuers take advantage of falling rates to refinance early, forcing investors to reinvest proceeds at lower yields just as the bond’s carry was becoming attractive. Downgrade risk is the slow-burn threat. If an agency moves a name from A to BBB, it tightens portfolio constraints, while a fall into high yield can trigger forced selling and sharp spread widening. And then there’s event risk, which is the unpredictable stuff, something we cover in more detail later on.

The Corporate Bond Market Structure

Unlike equities, which trade on centralised exchanges, corporate bonds trade over-the-counter (OTC) through a decentralised network of dealers and institutional investors. That structure makes it deep and liquid for large players, but opaque for everyone else.

At its core, the market is split into two tiers: primary issuance and secondary trading.

In the primary market, companies issue new bonds through underwriters (typically investment banks) that syndicate the deal to investors. Pricing is usually set relative to the Treasury curve or to swap rates, with spreads reflecting credit risk and investor demand.

Once sold, those bonds enter the secondary market, where they trade between dealers, asset managers, insurance firms, hedge funds, and other institutions.

Unlike equities, there’s no central order book. Instead, price discovery happens through dealer runs, platforms like Bloomberg, and direct RFQs.

As a quick historical detour: When Michael Bloomberg founded his company in 1981, his vision was to solve manual problems in the bond markets. Bloomberg’s idea was to create a data terminal for bond traders, giving them real-time prices, yield calculations, and analytics directly on-screen. The first product, called the Market Master terminal, was sold to Merrill Lynch in 1982. It let users analyse Treasuries, corporates, and mortgage-backed securities, calculate yields and spreads instantly, and price securities off the yield curve. This was a massive leap from the manual calculations traders used before.

The big dealers (JPMorgan, Citi, Goldman Sachs, etc.) act as liquidity providers, making markets for clients but also holding inventory risk. For large trades, liquidity depends heavily on dealer balance sheet capacity and market sentiment. That’s why bond liquidity can vanish quickly in times of stress: there’s no standing bid if dealers step back.

The market itself is vast and segmented. The IG universe forms the backbone of corporate debt, dominated by long-term investors like pension funds and insurers.

The HY segment attracts hedge funds and credit specialists seeking higher returns and higher risk. There’s also a growing market for private placements, 144A bonds, and ESG-linked issuance, each catering to specific investor bases.

On the trading side, technology has been reshaping the structure. Platforms like MarketAxess, Tradeweb, and Bloomberg’s ALLQ have brought partial transparency and electronic execution to what was once a phone-based market. For example, you can see the ALLQ strip for the JPM 2038 bond:

The shift to e-trading has made TRACE (FINRA’s reporting system) data more visible, but spreads and liquidity conditions still vary sharply by issuer, maturity, and rating.

Trading and Hedging Credit Risk

Institutional investors use multiple instruments to express or hedge credit exposure from corporate bonds.

Credit Default Swaps (CDS)

CDS contracts are the synthetic counterpart to cash bonds. A CDS buyer pays a periodic premium to hedge against default, while the seller collects that spread in exchange for taking on credit risk. They allow investors to go short credit, something that’s hard to do efficiently in the cash market. Hedge funds and bank trading desks use CDS for tactical trades around earnings, M&A, or idiosyncratic stress, while real-money accounts use them to fine-tune portfolio risk without selling bonds.

CDX Indices

CDX indices package dozens of CDS contracts into a single tradable exposure—investment-grade (CDX IG) or high-yield (CDX HY). This gives institutions an efficient way to take or hedge systemic credit risk. For macro funds, CDX is the instrument of choice when trading broad credit cycles, such as during periods of tightening or easing in financial conditions. Dealers use them to warehouse or offset client flow, while relative value funds arbitrage index vs. single-name spreads (“index basis”). Their liquidity and tight bid-ask spreads make them a benchmark for credit sentiment at the index level.

Bond ETFs

Bond ETFs, such as LQD (investment-grade) and HYG (high-yield), have become the liquid face of credit beta. They trade on equity exchanges, offering intraday liquidity that the underlying bonds lack. Institutions use them to quickly add or reduce exposure, hedge credit books, or proxy benchmark performance. In stress periods, ETFs can trade at discounts or premiums to NAV, effectively providing a price discovery mechanism when cash markets freeze. For that reason, LQD and HYG have become essential tools for both tactical positioning and liquidity management in modern credit portfolios.

Different ETFs have different allocations. LDQ is shown:

Until recently, institutional investors had no exchange-traded product that tracked credit spreads directly. Bond ETFs like LQD and HYG offered partial solutions, but they carry embedded interest rate risk and don’t isolate credit as cleanly. CDX indices solved that issue in OTC markets, but they require ISDA agreements and are less accessible for smaller institutions or systematic traders.

That’s where Credit Futures come in...

Credit Futures

Launched by CME Group in 2023–24, CME Credit Futures constitute a significant innovation bridging cash bonds, ETFs, and CDS.

They’re futures contracts that are cash-settled to market-leading Bloomberg corporate bond indices. These effectively turn broad credit exposure into a transparent, margin-efficient, and easily tradable instrument. Each contract reflects a standardised credit spread level, allowing investors to gain or hedge credit beta with the simplicity and accessibility of futures rather than dealing with bilateral CDS contracts.

CME offer four main futures contracts, with one based on the Bloomberg US Corporate Investment Grade Index (IQB) and another on the Bloomberg US Corporate High Yield Very Liquid Index (HYB). Duration-hedged versions are also available. Growth in open interest over the past year has been significant, to say the least:

Notable Events and Current Market Dynamics

Three forces shape corporate bond performance:

Rates and Treasury yields drive the risk-free anchor. After all, the Corporate Yield = Treasury Yield + Credit Spread. When Treasury yields move, the entire corporate curve shifts with them, regardless of whether the company’s fundamentals change.

Spreads reflect credit conditions and investor appetite. We’ve spoken already about how credit spreads are calculated, but in short, we can say that IG spreads are more sensitive to macro growth and rate volatility, while HY spreads are more sensitive to default expectations and market sentiment.

Liquidity amplifies moves in both directions. Liquidity determines how quickly and how violently prices adjust, which is a major factor given that corporate bonds are OTC.

Enron, WorldCom, and the Early-2000s Credit Reckoning

In the early 2000s, the collapse of Enron and WorldCom exposed deep weaknesses in corporate accounting transparency. These weren’t fringe high-yield names; they sat in the investment grade universe, widely held in pension portfolios and core bond funds. When the fraud came to light, corporate spreads widened sharply across the market, not just in the companies involved.

Investors learned that credit ratings could be wrong, financial statements could be manipulated, and the concept of “investment grade safety” depended heavily on trust. This period reshaped credit research. Analysts became more focused on cash-flow durability, governance quality, and off-balance-sheet liabilities. It marked the moment corporate bond markets internalised that credit spreads also price uncertainty, not simply default probabilities.

The 2008 Financial Crisis and Liquidity’s Breaking Point

The failure of Lehman Brothers triggered a complete breakdown in the global financial system. Banks stopped lending to each other, dealers stopped committing balance sheet capital, and credit markets froze. Spreads didn’t just widen, but blew out to levels implying mass corporate default. Yet many companies were fundamentally solvent.

With dealers unwilling to intermediate and forced sellers needing cash, even healthy corporate bonds traded like distressed assets. It was only when central banks intervened—cutting rates, backstopping banks, and flooding markets with liquidity—that credit markets stabilised. This episode permanently changed market microstructure: liquidity could no longer be assumed, and the corporate bond market became more flow-driven and more policy-sensitive than ever.

The 2011 European Sovereign Debt Crisis

The European debt crisis began as a sovereign problem but quickly bled into corporate credit, especially in the banking sector. Investors questioned whether countries like Greece, Portugal, and Spain could repay their government debt. European banks, large holders of sovereign bonds, saw their own credit risk re-priced as default fears spread. The EU IG 5-year CDS spread hasn’t traded close to the 2011 levels since:

Even sound non-financial corporations were caught in the contagion because funding markets tightened and cross-border capital retreated. When governments wobble, corporate credit spreads widen, even without a deterioration in earnings.

The 2014–2016 High-Yield Energy Bust

When oil prices collapsed from $100 to below $30, high-yield markets were heavily exposed to US shale producers, who relied on continuous capital market access to fund their drilling operations. Spreads in energy-related high-yield bonds widened over 1,000bps, defaults surged, and recovery rates were weak. The sector impact was enough to pull the HY market higher, demonstrating that sector risk can dominate broad-market credit spreads.

The December 2018 Liquidity Air Pocket

Late 2018 saw a sharp and sudden tightening of financial conditions as the Federal Reserve continued raising rates while global growth momentum slowed. Investor sentiment flipped risk-off, and liquidity in corporate credit evaporated. Primary issuance in high yield dropped to zero for the entire month, a phenomenon that had not occurred since the financial crisis. Prices gapped lower, not because solvency risks changed, but because dealers stepped away, and sellers found few bidders. This episode showed that the dealer's balance sheet is now a constraint in modern bond markets. When volatility rises, liquidity disappears quickly.

March 2020: The COVID Credit Shock and Fed Backstop

At the onset of COVID, fears of a sudden stop in global economic activity caused credit spreads to spike violently. High-yield traded like distressed debt, and even fundamentally strong investment-grade companies saw their financing costs soar. Liquidity froze until the Federal Reserve announced the Corporate Credit Facility, committing to buy corporate bond ETFs and individual issuers if needed.

The psychological effect was immediate: spreads tightened sharply, issuance reopened, and companies raced to term out debt at record size. It was one of the clearest demonstrations in history that policy backstops can override market panic and re-anchor risk pricing almost overnight.

Bringing It Around to 2025

We laid out the current dynamics of Big Tech’s entrance to the credit world, and after reading the above primer, you have a good sense of how credit markets work. Now, let’s take a further look at current market dynamics.

Equity markets have ridden the AI wave to record highs, but bond investors are taking a more circumspect view, less concerned about betting on AI’s future or Big Tech’s success, and instead focused on downside risks.

The deluge of tech issuance hitting the corporate bond market is not only headline-grabbing but also a stress test for how credit absorbs unprecedented capex cycles. The sheer scale of data-centre financing risks creating supply indigestion, particularly in dollars, but increasingly in euros as US tech issuers push into underpenetrated markets. The lure of lower yields in the euro area is hard for US companies to resist, and given current rate expectations on the two sides of the pond, it’s expected that the inward flow of reverse Yankees (bonds issued by US firms in a foreign currency) will continue.

Tech issuers have also shown themselves to be less price-sensitive, given the strategic importance of the AI buildout. This is a new dynamic for credit markets and one that will likely reprice credit far beyond the tech complex.

At the lower end of the quality spectrum, the picture is trickier. While originally limited to investment-grade infrastructure and real estate financing, high-yield issuance is also spilling into the junk debt markets. Cipher Mining and TeraWulf (crypto mining companies that are pivoting towards the AI expansion) have recently sold high-yield debt to fund data centre expansion. Applied Digital said it plans to sell $2.35 billion of five-year green senior secured notes to fund additional computing capacity.

If the market digests too much speculative supply too quickly, spreads on this cohort can widen sharply and force other issuers to match concessions. Big Tech hyperscalers are one thing, but a glut of supply of lower-quality names in the AI space might be too much for markets to stomach.

Meanwhile, core investment-grade spreads have remained surprisingly well-anchored this year, cushioned by heavy inflows hunting for yield. For Europe, Big Tech’s expanding presence remains a net positive, while investors remain underweight to high-quality US tech credit, and euro demand can comfortably absorb more paper as long as credit profiles remain intact. But the forward picture is shifting. IG spreads may drift toward a 100–110bp “base range” by 2026 (see chart below of recent trend higher) as issuance hits new records and the market re-prices supply pressure, duration risk, and a more uncertain profitability path for AI capex.

Refinancing risk and macro market destabilisation are also acute risks. If markets stay calm and yields drift lower, refinancing debt is straightforward. But if the macro environment gets shaky (slower growth, higher inflation, policy uncertainty, an equity selloff, or just a shift in risk appetite), the cost of issuing new bonds spikes and investor demand becomes more selective. Lenders, wary of the risks that surround the AI boom, are hedging their exposure via single-name CDS, betting selectively against future monetisation.

This is the backdrop into which every corporate issuer now walks. Lower-quality firms with concentrated revenue bases or data-centre timelines will find required yields creeping higher. Higher-quality tech issuers may still enjoy outsized demand, but this introduces new distortions for the rest of the market. The centre of gravity in corporate credit is shifting toward AI-funded capex cycles, and no sector remains insulated.

Ultimately, corporate credit is a forward-looking market. Successful participation requires understanding not only what a bond pays, but why it pays that spread, how its risks evolve across time, and what happens when liquidity dries up. In that sense, corporate bonds offer more than income. They offer a lens into how the financial system prices risk itself.

You can read more of our Global Primer Series here.

We hope you enjoyed the insights on corporate bonds. If so, please like and repost/share this article to show your support. As always, comments and opinions to keep the conversation going are welcome in the comments.

AP

SOFR options are arguably the purest expression of monetary policy risk in global markets. While SOFR futures have become the benchmark for short-term US dollar interest rates, options on these futures allow traders to express views on the distribution of outcomes, not just the level.

They are where rate-cut hopes get repriced in seconds, and where convexity lives. When the market wants to place a low-cost, leveraged bet on the Federal Reserve doing something unexpected, this is the first stop.

In this primer, we’ll run through:

- SOFR Futures
- SOFR Option Specifications
- Pricing and Vol Dynamics
- Key Participants
- Trading Strategies
- Risks and Considerations

Subscribe now

The Underlying: SOFR Futures

SOFR (Secured Overnight Financing Rate) futures are contracts that reflect the compounded average of daily SOFR overnight rates over the contract’s period. Typically, these are traded as three-month contracts, but other tenors exist.

• Quoted as 100 minus the implied interest rate, e.g. 95.00 = 5.00% implied (the IMM Index form)

• One tick = 0.0025 (0.25bp) = $6.25 per contract

• Deep liquidity out to ~3 years (e.g. Z5, H6, M6)

For example, the SFRZ5, trading at 96.33, is the December 2025 SOFR futures contract, showing the market implying a rate of 3.67%:

Options trade on these futures, not directly on SOFR itself.

If you want a deeper rundown on SOFR itself, you can read our previous primer on STIR Trading.

SOFR Option Specifications

SOFR options are listed on CME and are structured as American-style options on both three-month and one-month SOFR futures contracts.

They can be exercised early and settle into the corresponding futures contract on expiration (for standard options). Each option represents the right, but not the obligation, to buy (in the case of a call) or sell (in the case of a put) the underlying futures at a predetermined strike price.

Remember that due to the IMM quoting, if you expect interest rates to fall more than currently expected by the market, you’d buy a call. If you expect them to rise, you’d buy a put.

Strike prices are listed in fixed increments, usually 25bps apart, although strikes closer to the prevailing futures price are often listed in finer increments to meet demand for at-the-money liquidity.

Option premiums are quoted in futures ticks, with one tick the same as the underlying futures contract. This makes the premium translation between price and dollar terms straightforward for traders managing risk across both options and futures books.

Expiration dates are scheduled either weekly or monthly, with each option expiring on the Friday immediately preceding the start of the underlying futures delivery month. This creates a clean handoff: once exercised or expired, the position simply rolls into the active futures contract, allowing traders to manage duration risk without operational frictions.

However, you can also trade mid-cuve options, which have an expiry date well in advance of the futures contract. For example, you could buy a December ‘25 expiry mid-curve call on a March ‘26 SOFR future.

When exercised, SOFR options settle into the corresponding futures contract. Those futures are themselves cash-settled at expiry against the compounded SOFR index; there’s no physical delivery of an underlying bond. This structure eliminates delivery risk and makes the product clean for hedgers and speculators.

Below is a useful visual depiction of available options based on expiry dates relative to the underlying three-month SOFR future settlement dates.

Pricing & Volatility Dynamics

SOFR Options are priced a bit differently from what most equity or FX traders are used to. Instead of quoting implied volatility in the usual lognormal terms (percent moves), they’re quoted in normal volatility, measured in basis points.

That distinction matters a lot. Normal vol describes the expected absolute move in interest rates, not the percentage move in price. For example, a 50bp normal vol on a one-year SOFR option means the market expects rates could swing about 50bp over a year, not 50% of their level.

This approach makes sense for short-term interest rate markets. Rates can sit close to zero or even go negative, where lognormal models break down because percentage changes become meaningless. Normal vols stay stable and well-behaved at any level, which makes risk management cleaner. It also means option Greeks like delta and vega behave more intuitively: vega is linear to rate changes, not proportional to price returns.

The big takeaway is that when you see SOFR option vols rising along the curve, it’s signalling that traders expect a wider range of possible rate outcomes—not that they expect wild percentage returns. It’s a direct measure of policy-rate uncertainty in basis points, which is exactly what matters for this part of the market.

Vol Surface Dynamics

Consider the below vol term structure, with 25 delta puts in white and 25 delta calls in red. The skew is shown in the bottom pane.

Even without trading, we can get valuable information from this. What we’re looking at is not a single tenor’s continuous vol curve, but rather a sequence of expiries stitched across the SOFR strip (whites, reds, greens, blues). Taken together, they show a familiar pattern: implied vols are higher in deferred contracts than in the front, a patchwork term structure that reflects the market’s pricing of greater long-term uncertainty. Medium-dated vols pick up sharply, and skew leans to calls.

In rates, skew is usually such that receivers (rate puts, calls on price) are richer than payers, because crash scenarios map to sharp rate cuts. That’s why implied vol shows ‘negative skew’ even though it manifests as calls (lower rates) trading above puts.

This suggests hedgers and speculators are less worried about sharp Fed hikes (tail risk priced out), and more worried about persistent or renewed downside in rates.

Key Market Participants

In the past, SOFR options were traded primarily in the institutional market. However, this is changing, with a growing interest from the sophisticated retail segment emerging. Of course, notional volumes are still heavily skewed towards institutions.

Hedge Funds

These funds use the options space to express directional policy views or tail hedges. They give traders exposure to short-term funding costs, effectively acting as a clean and liquid proxy for Fed policy expectations. So the fund might use them offensively to express directional views on how many cuts or hikes the Fed might deliver.

The appeal of using options instead of the futures contract lies in their convexity; small moves in implied policy can generate outsized gains in well-structured option trades. For example, a hedge fund anticipating a more dovish pivot than markets currently price can buy calls on SOFR futures, capturing upside if rate cuts accelerate. Conversely, selling volatility through SOFR options can monetise elevated hedging demand when markets are nervous. This makes them powerful tactical tools, especially around key data releases and Fed meetings.

Banks/Dealers

Banks and dealers1 are the ones who keep the market running. They’re not just passive intermediaries; they’re the primary liquidity providers, market makers, and risk warehousers. When asset managers, hedge funds, or corporates want to buy convexity or hedge policy risk, it’s the dealing desks at tier 1 banks who quote the prices, take the other side, and manage the flow.

These desks operate as volatility hubs: they run large books of SOFR options, dynamically hedging the gamma and vega they absorb, and using swaps, futures, and other options to offset risk across the curve. Their constant two-way quoting keeps bid-ask spreads tight, which is what allows the market to handle size. It’s also why implied vol surfaces in SOFR are smooth (most of the time), because they reflect dealer pricing models as much as end-user demand.

While many of these desks are housed within banks, it’s worth noting that several of the largest designated market makers are independent trading firms. In fact, non-bank players often take down as much, if not more, flow than the traditional bank dealers. Together, they make up the core liquidity provision that keeps SOFR options tradable at scale.

Because these banks are effectively long liquidity and short optionality most of the time, they shape how the market reacts to shocks. When volatility spikes, dealers can pull back or reprice aggressively, which amplifies moves. And when volatility fades, they step back in, tightening spreads and rebuilding inventory. In short, banks and dealers are the backbone of the SOFR options market.

Asset Managers

SOFR options are becoming an increasingly useful tool for asset managers, who approach the market from a very different angle than dealers. They use options to shape the risk profile of their portfolios. For large fixed-income managers, SOFR options offer a clean way to hedge policy-rate exposure without having to unwind core bond holdings. A well-placed payer structure can protect against a rates selloff, while receivers can cushion the impact of surprise Fed cuts.

These players are typically slow-moving but size-driven. They come into the market with large notional needs tied to duration targets, liability matching, or regulatory capital requirements. Because of that, their flows can move implied volatility: when they build protection, vols tend to richen, and when they unwind, dealers are left long gamma and vol softens. They also tend to prefer longer-dated tenors, which adds depth and liquidity to the back end of the curve.

Corporate Treasury Desks

Although not a big player, there’s steady participation from corporate treasury desks, though their role is more episodic and purpose-driven than the flow-driven presence of banks or asset managers. Treasuries use SOFR options as a risk management tool. Their exposure comes from floating-rate debt, commercial paper programs, and planned funding needs, with options that give them a way to cap or floor borrowing costs without locking in fixed rates outright.

When a treasury desk fears rising short-term rates could hit interest expense, they’ll often buy payer options to set a ceiling on costs. Conversely, if they’ve got excess cash and want protection against falling reinvestment yields, they may pick up receiver options. These flows are typically chunky and infrequent, tied to funding cycles or major corporate events like acquisitions or large bond issues.

Because they operate on long decision cycles and focus on downside protection, corporates aren’t a constant source of liquidity. But when they show up, they matter.

Trading Strategies

Besides hedging, SOFR options are used for a range of directional and volatility-driven strategies:

Vanilla Calls/Puts

The bread-and-butter trade for retail and macro funds alike has been outright vanilla options on deferred contracts. The logic is simple: if you think the Fed is going to be cutting rates faster than the curve implies, buying calls a few quarters out gives you cheap convexity. It’s a levered way to play the easing cycle without taking duration outright, and the upside looks outsized if the policy pivot comes early. The reverse can be applied if an investor feels rates will stay higher than anticipated, buying puts in the process.

Calendar Spreads

Timing matters, and that’s where calendar call spreads come in. Traders buy calls in one expiry while selling calls in a later one, effectively betting the easing cycle kicks off earlier than priced. If the Fed moves a quarter or two sooner, that front-end call explodes in value while the back-end short leg bleeds. It’s a way of monetising not just the direction of rates, but the timing of policy shifts. Turning a vanilla option into a spread trade also limits the premium outlay to begin with.

Straddles/Strangles

For those less interested in direction and more in fireworks, straddles and strangles are the go-to. A straddle (buying a call and put at the same strike) or strangle (buying out-of-the-money call and put) is a pure volatility bet. Rates can move up or down; you just need them to move enough. These trades have spiked in popularity around FOMC meetings, jobs reports, and CPI releases, where realised volatility often overshoots implied. For traders conditioned by equity earnings straddles, SOFR options are a natural extension into macro vol.

Risk reversals

Finally, risk reversals are all about skew. In SOFR options, puts have historically been cheaper than calls, reflecting the market’s bias toward easing. Traders who think the market has the skew wrong flip the trade (selling expensive calls to fund cheap puts or vice versa). It’s a way of taking a stance not just on direction but on how the market is pricing risk asymmetry. In practice, these are favoured by more sophisticated retail accounts and smaller prop shops looking to capture mispricings in implied skew.

Risks & Considerations

There are numerous disclaimers and risks that should be included in a Primer discussing options on futures contracts, but we have decided to expand on a few that are definitely worth noting.

Theta Decay

Time value bleeds fast in short-dated contracts, and with SOFR options now listed in weekly cycles, traders often underestimate how quickly premium erodes if the expected move doesn’t arrive on schedule. It’s why a perfectly “right” Fed call can still lose money if the timing is off on the expiry for the option.

Liquidity Pockets

Another overlooked issue is liquidity. While the front-month, near-the-money strikes trade deep and liquid, venture too far out-of-the-money or into deferred expiries and depth thins quickly. Spreads can widen, fills can slip, and unwinds can cost more than expected. For retail traders stepping into this market, that risk is amplified. There’s no Robinhood-style order book smoothing here.

M2M Swings

It seems obvious, but P&L can swing violently with every tick in implieds. A book of short-dated calls or straddles looks cheap on entry, but can be subject to large intraday swings. It’s the classic “convexity cuts both ways” problem.

Implied vs Realised Vol

SOFR options often price in hefty event premiums, especially around FOMC or NFP weeks. If realised volatility underdelivers, traders are left overpaying for insurance that never pays out. In practice, this is the single biggest drag on speculative P&L in the product.

Discipline around entry levels and understanding when implied vol is already “rich” is what separates the winners from the churn. Inexperienced traders often buy cheap out-of-the-money options, only to watch them expire worthless as the Fed moves slower than expected.

Key Takeaways

• SOFR options are the market’s sharpest instrument for trading Federal Reserve policy risk

• They offer convex payoffs but demand precise timing and sizing

• Volatility dynamics (skew, term structure) reveal market expectations and fear

• Hedge funds, along with banks and dealers dominate flow

• Like all options, theta decay, IV crush and M2M swings are key risks to be aware of

SOFR options are a tactical tool, not a passive hedge. They are where monetary policy conviction meets market leverage, with the market becoming an area of increasing focus for both retail and professional investors alike.

We hope you enjoyed this primer and insights on SOFR options. Please leave a like to show your support. As always, comments and opinions are welcome in the comments.

AP

Volatility is the price of uncertainty. But in modern markets, it’s not just a consequence, it’s a product.

From VIX to MOVE to CVIX, volatility indices and derivatives sit at the crossroads of risk pricing, macro signals, and trading opportunities. They’re also some of the most misunderstood tools in the global financial toolkit. This primer demystifies the key players in the volatility space and lays out how traders, hedgers, and macro investors use them.

Subscribe now

This primer runs through:

- How to measure volatility
- Key volatility indices
- Major historical events
- How to use volatility products
- Key participants in the ecosystem

Measuring Volatility

Before we get into specific gauges, consider the two forms of volatility that are generally relevant for investors.

Realised Volatility (RV)

This is a backwards-looking measure of how much an asset’s price actually moved over a past period (e.g., last 10, 30, or 90 days). It’s calculated directly from historical price data

How it’s calculated:

1. Take historical prices (usually daily closes).

2. Calculate daily returns (Rt):

   Rt =ln⁡ (Pt / Pt−1)

3. Square each return (to remove direction).

4. Average them over the lookback window.

5. Take the square root and annualise (multiply by √252 for daily data).

Example: If the standard deviation of daily returns is 1%, the annualised realised vol is:

0.01 × 252 ≈ 15.9%

The key point is that RV is observable, but it’s about the past, not what traders expect next.

Implied Volatility (IV)

This is a forward-looking measure—the level of volatility the options market is pricing in for the future. It’s backed out from option prices using an options pricing model like Black-Scholes.

Let’s say you get a price from a market maker for an option. From the price, you can strip out elements that you already know that go some way to making up the price, such as the current underlying price, the strike level, the time to expiry, etc. By removing all of these, you can solve for the only unknown—implied volatility.

It’s the IV pulled from option prices that form the base when trying to quantify volatility for the future.

Below is an example for EUR/USD, showing the one-month historical vol compared to the one-month implied vol:

Volatility Gauges

The VIX: Equity Volatility’s Global Poster Child

What it is: The VIX is a model-derived measure of expected 30-day S&P 500 volatility, calculated from out-of-the-money SPX options. It’s quoted as a single number, but it’s not a dollar price; rather, a volatility percentage. The figure is in annualised percentage points, which you can convert to the implied one-month equivalent (e.g., VIX at 20 = implied 1-month SPX vol of ~5.8%).

How it works: Rather than tracking realised volatility, the VIX uses a weighted strip of SPX options to interpolate expected variance. It’s a forward-looking proxy for how much turbulence markets are pricing in.

Why it matters: The VIX reflects not just hedging demand, but also liquidity, positioning, and the cost of convexity. It’s also the backbone of a suite of ETPs and derivatives, from VIX futures to inverse vol ETFs.

The MOVE: Treasury Volatility’s Revival Signal

What it is: The MOVE Index (Merrill Option Volatility Estimate) is the bond market’s equivalent of the VIX. It measures the implied volatility of 1-month Treasury options across 2y, 5y, 10y, and 30y tenors, weighted to reflect market impact.

Why it matters: MOVE spikes often precede or coincide with funding stress, policy surprises, or convexity hedging cascades. In a world where macro risk increasingly lives in fixed income, MOVE is a critical signal of systemic stress.

Relationship to VIX: Historically, MOVE and VIX were positively correlated. Post-GFC, that relationship broke down. In 2023–2024, MOVE surged while the VIX stayed muted—a regime defined by bond volatility without equity panic.

The CVIX: Currency Volatility’s Black Box

What it is: CVIX refers to the Deutsche Bank FX Volatility indicator. It’s a synthetic index created by DB to track implied volatility in G10 foreign exchange markets. It takes 1-month at-the-money implied volatilities from FX options across a basket of liquid G10 currency pairs. Each pair is weighted by its global trading volume and liquidity so that bigger pairs (EUR/USD, USD/JPY) have more influence. The individual vols are then combined into a single, trade-weighted average, producing one daily number.

Why it’s underfollowed: CVIX gets less attention due to decentralisation. No single currency “index” defines the space. But for global macro traders, implied FX vol is often the cleanest expression of regime shifts, especially in EM.

Why it matters: FX vol is sensitive to interest rate differentials, central bank divergence, and geopolitical shocks. When FX vol jumps, it often signals cross-asset contagion brewing under the surface.

Key Historical Events: When Volatility Spoke Loudest

2008, GFC

During the Global Financial Crisis (GFC), volatility surged, with the VIX and MOVE registering some of their highest readings on record. The VIX spiked to an intraday high of 89.53 on October 24, 2008, and closed as high as 80.86 on November 20, 2008. That was nearly triple its 2007 average level of 17.5, underscoring the scale of equity-market panic.

The MOVE index followed a similar trajectory in the rates market, climbing from a pre-crisis low near 51 in May 2007 to a historic high around 264 in October 2008. This reflected extraordinary uncertainty around interest rates, credit conditions, and liquidity.

2013, Taper Tantrum

During the 2013 taper tantrum, market volatility followed a markedly different pattern than in more acute crises.

Equity-market fears, measured by the VIX, rose only modestly. In fact, the VIX remained generally below 20 throughout May‑July 2013—even as Fed tapering loomed—indicating that equity volatility stayed relatively contained.

Meanwhile, uncertainty rippled across emerging markets rather than US equities. After Federal Reserve Chairman Ben Bernanke hinted at scaling back QE in May 2013, emerging-market assets faced sharp corrections: exchange rates and stock indices tumbled, and bond yields and risk premiums widened substantially.

Within weeks, the S&P 500 dropped around 8%, world stock indices fell by nearly 10%, and EM currencies and equities declined by approximately 5% and 15%, respectively; at the same time, the U.S. 10‑year Treasury yield surged from around 2% to roughly 3%.

While equity volatility stayed muted, the MOVE index surged. Even though the CVIX only rallied modestly, the emerging market FX equivalent gauge saw an outsized move, as shown below:

2018, Volmageddon

Volmageddon in February 2018 was triggered by a violent unwind of the short-volatility trade that had flourished during the unusually calm markets of 2017. For most of that year, equity volatility was historically low, with the VIX spending months in single digits. This lulled investors into selling volatility—both directly, through shorting VIX futures, and indirectly, through inverse-VIX exchange-traded products (ETPs) like XIV and SVXY—to capture steady premium income.

The spark came on February 5, 2018, when U.S. equities suffered a sharp drop: the S&P 500 fell nearly 4% in a single session, its largest daily decline since 2011. As the market fell, the VIX more than doubled—its biggest single-day percentage jump on record—moving from around 17 to above 37. That spike in implied volatility caused massive mark-to-market losses for short-volatility positions. For inverse-VIX ETPs, the problem was structural: their daily rebalancing meant they had to buy VIX futures into a volatility spike to maintain exposure. This created a feedback loop—forced buying of VIX futures drove volatility even higher, which in turn forced more buying.

The result was a sudden, disorderly repricing in the volatility complex. XIV, one of the most popular inverse-VIX products, lost over 90% of its value in a day and was subsequently liquidated. The episode revealed just how fragile the ecosystem of short-vol strategies had become, and how quickly structural positioning can turn a garden-variety equity pullback into a self-reinforcing volatility explosion.

2020, COVID Crash

What made the COVID volatility episode unique was its breadth and simultaneity: equities, bonds, and currencies all experienced extreme implied and realised volatility at the same time, breaking the usual negative correlations that underpin multi-asset hedging strategies.

In equities, the VIX surged from a calm pre-pandemic level in the low teens in February 2020 to an intraday peak of 85.47 on March 18, 2020, just shy of its all-time GFC highs. The move was not just about the magnitude—it was the speed. Within a matter of weeks, equity implied volatility went from pricing orderly markets to reflecting full-blown systemic stress.

In fixed income, the MOVE index also spiked to extremes, breaching 160 in mid-March 2020. That jump was fueled by extraordinary swings in U.S. Treasury yields, driven by a scramble for cash, dysfunction in Treasury market liquidity, and aggressive repricing of Fed policy expectations as the central bank delivered emergency rate cuts to zero. MOVE’s surge was notable because Treasuries usually act as the calm, stabilising leg of a portfolio—yet in March 2020, they became a source of volatility themselves.

The CVIX spiked sharply as well, reflecting turbulence in currency markets as investors unwound carry trades, sought USD liquidity, and navigated unprecedented central bank and fiscal interventions. Dollar funding stress, revealed through widening cross-currency basis swaps, compounded the volatility in FX.

How Traders Use Volatility Products

There are an almost limitless number of permutations as to how and why someone might want to trade using volatility as a direct or indirect proxy. Below are some of the main ones from our experience.

VIX Calls To Hedge

When volatility is underpriced relative to event risk, or simply to hedge an existing portfolio, buying VIX calls and long VIX futures can provide asymmetric protection against equity drawdowns.

Calls offer defined-risk convexity, with payoff profiles that expand rapidly during vol spikes, making them well-suited for tail hedging when gamma and vega sensitivity are desired.

Futures provide more linear exposure to expected VIX levels at expiry, useful for tactical positioning ahead of known catalysts. The trade-off is that futures carry roll costs in contango and can bleed during quiet periods, whereas calls suffer time decay but avoid roll yield.

Sizing should be calibrated to the portfolio’s equity beta and expected vol beta, with notional chosen to offset drawdowns over the time period in question. In practice, traders often stagger maturities to reduce event timing risk and blend outright long calls with spreads to manage premium outlay while maintaining protection.

FX Options Structures

Traders don’t really use the CVIX as a way to exploit volatility movements. Rather, they use option structures on individual currency pairs that can benefit from a directional move in volatility.

For example, let’s say a trader takes a view that the implied volatility for the coming month on EUR/USD is too low. This could be due to events taking place during the next month, such as elections, central bank meetings or data prints.

They could buy a one-month ATM Straddle structure. This involves buying a 50 delta Call option and a 50 delta Put option, paying a combined upfront premium. This trade is direction agnostic. The trader doesn’t really care if the market moves higher or lower. But what he has isolated is the volatility element of the currency. He’s betting on a significant move (either way) and therefore a rise in realised volatility during the month.

Long Gamma Via Swaptions

Traders use long gamma positions via swaptions to protect against sharp, unexpected moves in interest rates—particularly when Federal Reserve policy uncertainty is high.

In practice, this means buying out-of-the-money payer and receiver swaptions on longer-dated swaps (often 5y–30y tenors) so that the position has high gamma exposure.

Gamma measures how quickly a position’s delta changes as rates move; being “long gamma” means the position gains sensitivity to the underlying rate in both directions as volatility increases.

When the Fed’s policy path is unclear—say, the market is debating whether the next move is a cut, a hold, or even a hike—long-end yields can react violently to incoming data or shifts in forward guidance. Long gamma positions thrive in such an environment because they benefit from large intraday or intraweek rate swings. For example, if you’re long a payer swaption on 10-year rates and yields suddenly spike on a hawkish Fed surprise, the option’s delta rapidly increases, and the mark-to-market gain from being long gamma can outweigh the premium cost. Conversely, if yields drop on a dovish pivot, a receiver swaption can capture the upside.

In many cases, traders mirror the earlier concept in FX structuring, namely buying straddles in long-end swaptions—owning both payer and receiver optionality—to create a direction-agnostic hedge that profits purely from realised volatility exceeding the implied levels at which the options were bought.

Equity Vol Dispersion Trades

An equity dispersion volatility trade is a relative-value position between index options and the single-stock options of its constituents, designed to isolate and trade correlation.

In the classic long-dispersion setup, the trader sells the index straddle or strangle and buys weighted straddles or strangles on the component stocks. This creates a position that is long single-name gamma and vega while short index gamma and vega, effectively betting that realised correlation will fall below the level implied in market pricing.

Because index volatility is a function of the average constituent vol and their pairwise correlations, a drop in correlation means individual stocks move more independently, causing single-stock options to outperform the index options on a hedged basis.

The P&L comes from this correlation differential as well as any relative mispricing in vol between the two legs, with traders often delta-hedging both sides to focus purely on the correlation exposure. Risks include sudden spikes in correlation—common during market stress—which can cause index vol to surge relative to single names, as well as the execution complexity and hedging costs of managing a large basket of single-stock options. In practice, many desks use a liquid subset of names to proxy the index and carefully size and hedge the trade to manage correlation shocks and vol term-structure mismatches.

Variance Swaps

A variance swap is an OTC product that gives you a pure, delta-neutral exposure to the realised variance (volatility squared) of an underlying over a set period.

At the start, both sides agree on a “variance strike,” which reflects the market’s current expectation for future volatility, squared. When the contract ends, the actual volatility that happened during the period is measured and squared to get the “realised variance.”

The difference between this realised variance and the strike determines who makes money: if actual variance is higher than expected, the trader who bought variance gets paid; if it’s lower, they pay the other side. Because variance is volatility squared, big market moves have an outsized effect on the payoff, which makes variance swaps very sensitive to sudden spikes in volatility. In short, it’s a pure way to trade the market’s choppiness, separate from any directional price bets.

Market Participants: Who’s In the Game?

Banks

Role: Primary market-makers in OTC volatility products (variance swaps, vol swaps, dispersion trades, exotics) and liquidity providers in listed volatility products (index and single-stock options, VIX derivatives).

Objectives:

- Facilitate client trades and earn bid–ask spreads.
- Warehouse volatility risk temporarily and hedge it through option markets or dispersion strategies.
- Run correlation books, volatility arbitrage books, and structured product hedges.

Typical Positions:

- Often short vol from selling to yield-hungry clients, hedged via option portfolios or other vol products.
- May be long vol opportunistically in stressed markets to offset structured product exposures.
- Pure vol trading jobs are rare these days, with most being incorporated into derivative structuring roles:

Hedge Funds / Volatility Arbitrage Funds

Role: Opportunistic traders in volatility mispricings, correlation trades, and tail hedges.

Strategies:

- All of the above trading strategies we ran through apply, ranging from dispersion ideas to hedging existing positions.

Profile:

- Often more nimble than dealers; will actively take the other side of structured product flows.
- Use vol as a tactical overlay—buying gamma around events, expressing convexity views.

Asset Managers / Pension Funds

Role: Large end-users of equity and index options, often as overlays on long equity portfolios.

Objectives:

- Hedging: Use long vol positions (protective puts, collars, VIX calls) to insure against drawdowns.
- Yield enhancement: Selling covered calls or put spreads to generate income, taking on short vol risk.

Impact:

- Their systematic overwriting programs create a steady supply of short-dated index vol.
- Demand for hedges can spike during uncertainty, pushing up index skew.

Retail / Structured Product Buyers

Role: End clients around the globe, particularly HNW individuals, frequently buy structured notes linked to equity indices with yield-enhancing features.

Mechanics:

- These products are created by selling options (short vol) to the client, with the dealer taking the other side.
- Dealers then hedge, often by selling vol in the listed market — adding to supply.

Risks:

- Selling structured products to retail can offer headaches when they go wrong, with accusations of misselling or the complexity of products being beyond what should be sold…

Volatility as a Cross-Asset Compass

Volatility products are more than fear gauges—they’re structured, tradable expressions of market uncertainty. Whether it’s VIX for equities, MOVE for rates, or CVIX for currencies, each tells a different story. Together, they offer a powerful cross-check on market complacency, dislocation, and where the next storm may be brewing.

Trading volatility products isn’t for the faint of heart. The complexity of using them in real life can be problematic unless you fully understand how to price them and risk-manage around them.

Yet ultimately, global markets are becoming increasingly choppy. So for cross-asset traders, understanding volatility signals—and how they interact—isn’t optional. It’s alpha.

AP

Disclaimer: This material is provided for educational and informational purposes only. It does not constitute investment advice, a recommendation, or a solicitation to buy or sell any security, derivative, or other financial instrument. Trading in volatility products—including, but not limited to, listed and OTC options, variance swaps, volatility swaps, VIX derivatives, and structured volatility strategies—involves substantial risk of loss and is not suitable for all investors. These instruments can be complex, highly leveraged, and sensitive to factors such as implied volatility, realised volatility, correlation, interest rates, and market liquidity.

Past performance is not indicative of future results. Market conditions can change rapidly, and strategies that appear effective under certain circumstances may result in significant losses under others. The examples and trading approaches described herein are illustrative only and may not account for transaction costs, bid–ask spreads, margin requirements, tax considerations, or operational risks.

You should carefully assess your financial situation, risk tolerance, and investment objectives, and seek advice from a qualified financial professional before engaging in any trading involving volatility products. The author(s) and publisher make no warranties, express or implied, regarding the accuracy, completeness, or reliability of the information provided, and accept no liability for any loss or damage arising from reliance on this material.

For all the focus on bond yields, equity volatility, and economic growth, the market’s clearest view on credit risk trades in a more specialised arena: the CDS market.

Short for Credit Default Swaps, CDS are derivative contracts that function like insurance on corporate or sovereign debt. They allow investors to hedge against default, speculate on credit deterioration, or construct relative value trades across the capital structure. In many cases, they serve as the most liquid expression of credit risk—more responsive than cash bonds, and more versatile than credit ETFs.

CDS markets are used by banks, hedge funds, insurance companies, asset managers, and macro desks. They play a critical role in price discovery, risk transfer, and credit portfolio construction. And during stress events, they often move before anything else does.

This primer walks through:

- CDS basics (mechanics, pricing)
- The role of CDS in hedging and trading
- Credit curves, capital structure arbitrage, and basis trades
- Key participants and use cases
- Notable dislocations and blowups
- Why CDS matters for market structure and liquidity

If you like FX basis, STIRs, or convexity in obscure derivatives, you’ll enjoy this one. If you don’t, well, this is how the big players hedge credit risk. It’s worth understanding.

Subscribe now

Understanding a CDS

At its core, a Credit Default Swap (CDS) is a bilateral derivative contract designed to transfer credit risk. One party (the protection buyer) pays a periodic premium to another party (the protection seller) in exchange for compensation if a predefined credit event occurs1.

The CDS is settled against a reference entity (a corporation or sovereign) and applies to a reference obligation, usually a specific bond issued by that entity. If the entity defaults, the protection seller compensates the buyer by delivering the defaulted bond (physical settlement) or paying the loss value (cash settlement).

CDS contracts are usually quoted in basis points (bps) per annum on the notional amount of protection. The wider the spread, the higher the reference entity’s perceived credit risk.

For example, let’s take an investment-grade rated corporation, JP Morgan. The chart (in bps) of a five-year CDS is below. The five-year maturity is widely considered the benchmark for pricing and liquidity in general in the CDS market.

The five-year is trading just above 43bps. This means that for every $1m notional bought, a 0.43% premium is paid annually. In reality, this premium accrues and is typically paid quarterly.

If JPM defaults within this period and the bond recovers at 40 cents on the dollar, the CDS pays out $600k (i.e., $1m × (1 – 0.40)).

JPM has a relatively low CDS spread, with the payoff versus annual premiums being considerably different. However, consider the historical five-year period for Carnival Cruises. In the aftermath of the pandemic, there was serious concern that the lockdowns and lack of travel could put the operator out of business.

In late 2022, the spread spiked above 20%, implying that the annual premium equated to one-fifth of the notional payout in the event of default. This sharp widening reflected heightened market concern around credit risk.

The five-year CDS is also a useful tool for comparing company creditworthiness. Although Carnival is currently operating under normal conditions, its spread remains elevated at 147bps, nearly four times that of JPM.

One might consider shorting the firm’s bonds to express a similar bearish credit view. While this approach can work in certain contexts, it’s important to contrast the two instruments to understand the differences in exposure, liquidity, and execution.

CDS vs. Bonds

CDS offers a purer and often more liquid expression of credit risk than the cash bond market. It isolates the default component, stripping out interest rate duration, repo financing, and coupon variability.

Because CDS contracts are standardised and trade over-the-counter (with central clearing increasingly common), they are more flexible instruments for hedging, especially when it comes to building or managing credit exposure across a portfolio.

Another key difference is speed. CDS spreads often move more rapidly than bond prices. In periods of market stress, traders tend to reach for CDS as the first instrument to price in credit risk, driven not only by liquidity but also by the efficiency and clarity of expressing directional views.

While bonds represent long exposure to credit (providing repayment unless the issuer defaults), CDS flip the structure. Buying CDS constitutes a short credit risk position, with the buyer profiting if the reference entity deteriorates or defaults. Conversely, selling CDS creates long credit risk exposure, in which the seller receives premium income in exchange for assuming the risk of a credit event.

This asymmetric payoff structure makes CDS a critical tool for hedging long bond positions, constructing macro strategies around credit cycles, or isolating idiosyncratic credit risk at the single-name level.

Pricing, Premiums, and Payouts

At its core, a CDS contract is an insurance agreement. The buyer of protection pays a regular premium; the seller receives that income in exchange for assuming default risk.

There are two main legs to a CDS trade:

- Premium Leg: The protection buyer pays a regular premium (quarterly, in most cases) for the life of the contract or until a credit event occurs.

- Protection Leg: If a credit event occurs, the seller compensates the buyer, typically by paying the difference between par and the post-default recovery value of the reference bond.

CDS Spreads and Premiums:

As mentioned above with JPM, on a $1m notional, that’s circa $4,300 per year or $1,075 per quarter. If no credit event occurs during the contract’s life, the seller keeps the premium, and the contract expires.

But unlike fixed-rate bond coupons, CDS premiums aren’t baked into the upfront price. The contract is marked to market daily, and values fluctuate based on spread movements, just like a bond or interest rate swap.

CDS spreads are also widely used in credit risk modelling, counterparty valuation adjustment (CVA), and pricing of structured credit products. The implied probability of default embedded in the spread provides a key input across trading, risk, and capital modelling functions.

To best explain the following points, we’re going to base it on the deal screen for the JPM CDS.

Upfront Payments and Credit Curves:

For investment-grade CDS (where spreads are relatively low), contracts typically trade close to par. This means the upfront payment is pretty minimal; the spread itself represents the market’s view of credit risk. In the case of JPM, the price is trading above par at 102.47. This means the CDS has positive value to the seller.

For high-yield or distressed names, spreads can be hundreds or even thousands of basis points. In these cases, the CDS may require a much larger upfront payment at inception to account for the fact that the contractual coupon (usually fixed at 100bps or 500bps, depending on the market convention) is lower than the actual market-implied risk.

In the deal screen, the standard coupon is shown as 100bps.

To clarify, the premium leg is fixed, but the upfront cost adjusts the trade to fair value based on prevailing market spreads.

Credit curves are typically upward sloping—that is, longer-dated CDS contracts tend to trade at wider spreads. As illustrated in the bottom-right chart, the JPM curve adheres to this convention. However, as with yield curves, credit curves can invert under stress, particularly when short-term default risk rises sharply.

An inverted credit curve can also signal dislocation in near-term funding conditions or elevated concern over default within a specific time horizon—insights not always captured in cash bond curves.

The deal screen also displays a purple cash amount of $27,000. This figure reflects the combination of the principal difference (current price - par value x notional) and accrued premium. Together, these components determine the total upfront cash payment.

The Credit Event and Settlement Process:

If the reference entity defaults, the CDS pays out. But not all “bad news” triggers a credit event. The rules are strict, codified by ISDA (International Swaps and Derivatives Association), and standardised across contracts.

There are three main credit event types:
- Bankruptcy
- Failure to Pay
- Restructuring

Once a credit event is confirmed, the CDS is settled in one of two ways:
- Physical Settlement: The protection buyer delivers an eligible bond (or loan) and receives par from the seller.
- Cash Settlement: More common in recent years. The CDS is settled based on the recovery rate, determined through an auction process overseen by ISDA.

The ISDA auction process has become the standard settlement method across most CDS markets. It establishes a transparent and consistent recovery rate, ensuring both buyers and sellers can exit with defined outcomes.

Recovery Rates and Payouts:

In the case of a credit event, the payout to the protection buyer equals the difference between the notional and the recovery value of the reference obligation.

Payout = Notional × (1 − Recovery Rate)

If a $1m CDS triggers and the recovery rate is 40%, the protection seller pays the buyer $600k.

Recovery assumptions are a key input into CDS pricing models. For IG names, recovery is often assumed around 40%, while HY names might price with a lower implied recovery (20–30%), depending on market conditions and debt seniority. This impacts the default exposure.

Default Exposure = (1- Recovery Rate) x Notional - Principal

The deal screen can toggle the recovery rate from the standard 0.40. Note that the higher the recovery rate, the lower the default exposure becomes:

0.4 RR

0.7 RR

This is logical and shows how the bond’s creditworthiness is factored in multiple ways when breaking down a CDS.

The market-implied CDS spread pricing is essentially the cost of insuring a bond against default, adjusted for the probability of default and expected recovery.

Mathematically: CDS Spread ≈ Probability of Default × (1 − Recovery Rate)

This identity holds at the core of CDS valuation and is a common shortcut used in the front office to estimate fair spread levels quickly. In reality, CDS pricing also reflects liquidity conditions, dealer positioning, and counterparty risk, but the spread-recovery-default triangle remains the bedrock.

Hedging and Trading with CDS

CDS contracts were designed initially as hedging tools. Over time, they evolved into active trading instruments used across credit markets, from directional views to structured arbitrage.

Hedging Credit Exposure:

The most straightforward application of CDS is to hedge bond holdings. Example: Consider a portfolio holding corporate bonds issued by JP Morgan. If the investor is comfortable with the interest rate exposure but concerned about potential spread widening or default risk, buying CDS protection on the bank offers a direct hedge. This approach avoids the need to sell the bonds (which may be illiquid, tax-inefficient, or operationally difficult to replace) while isolating and mitigating the credit risk component.

Doing so converts a credit-sensitive bond into something closer to a risk-free asset. The mark-to-market changes in the bond and the CDS should (in theory) offset each other as credit spreads move.

Look at how a drop in the price of the 2030 JPM bond saw a corresponding spike in the CDS:

Hedging via CDS is common among asset managers, insurance firms, and banks, especially under capital frameworks like Basel III, where credit risk charges can be actively managed through CDS overlays.

Speculating on Credit Moves:

Just as CDS can hedge a long bond, they can also be used to take a view on credit deterioration or improvement:

- Bullish on credit: Sell CDS, collect premium, and hope no credit event occurs.
- Bearish on credit: Buy CDS, and profit if spreads widen or the reference entity defaults.

Because CDS spreads are quoted in basis points of annual premium, the mark-to-market sensitivity of a CDS position is straightforward. The P&L from spread moves is easy to calibrate for a given notional amount, making CDS an efficient instrument for short-term tactical trades or long-term macro themes.

Capital Structure Arbitrage:

CDS can also express relative value views across an issuer’s capital structure. One common strategy involves going long the equity of a company while simultaneously buying CDS protection on its debt. This setup allows a trader to isolate discrepancies between how equity and credit markets price the same underlying risk.

In theory, equity holders are junior to debt holders in the capital structure and should demand a higher return (and price in more risk). But in practice, markets often diverge, particularly in volatile or distressed environments. When credit spreads widen sharply while equity prices hold firm (or vice versa), it can signal mispricing between asset classes.

Example: Let’s say JP Morgan is going through a stress event, and a trader might feel the bond market has overreacted to default risk, while equity markets are pricing in a faster recovery. A trader might fade the panic-driven CDS widening with equity puts in those cases. If they’re correct, they’ll profit from tightening the spread, but give up some of the total gains from losses from the puts. In reality, it becomes a market-neutral trade that aims to target pricing inefficiencies across the capital stack (credit views relative to equity sentiment).

Basis Trades:

Another strategy in CDS markets is the basis trade. We’ve written about this before in other asset classes, for example, in STIR markets.

For our specific case, we’re talking about exploiting the spread difference between a CDS and its reference bond. In theory, a bond’s credit spread should match the CDS premium (after adjusting for technical factors like accruals, curve, and repo funding).

But in practice, this relationship breaks down, especially during market stress, funding squeezes, or index roll periods. Traders take advantage by buying the cheaper of the two and selling the richer leg, locking in a spread that converges over time.

Let’s consider Carnival again, with a bond maturing in about five years. The screenshot below shows the spread analysis, with a 159bps yield over the corresponding US Treasury.

The five-year CDS is at 155bps, which is basically the same as the spread versus the Treasury when factoring in technical factors.

Yet for argument’s sake, let’s assume the CDS was trading at 100bps all in, making the bond’s credit spread higher than the CDS. A trader might buy the bond and simultaneously buy CDS protection, capturing the positive basis as carry and expecting the spread difference to tighten over time.

Curve Trading:

Credit curves, like yield curves, are not always linear. Take a look at the curve below for Apple, which shows a steeper gradient in the 2-5 year space while flattening out in the longer term.

CDS traders often take curve views by going long one tenor (e.g., 5-year CDS) and short another (e.g., 10-year CDS) on the same reference entity.

Key Participants in CDS Markets

A mix of institutional players drives the CDS market, but dealers and hedge funds are the most active and influential participants. Other entities, such as asset managers, insurers, and corporates, engage in more selective or secondary roles.

Dealers and Banks are the primary liquidity providers. They make markets in both single-name and index CDS, manage client flow, and warehouse credit risk. CDS desks typically sit within credit trading or macro businesses and often balance directional views with hedging needs. Interdealer activity also helps set pricing benchmarks, especially around index rolls and event-driven situations.

Hedge Funds are active across speculative and relative value strategies. They use single-name CDS for outright credit views, index CDS to express macro themes, and structured trades like curve steepeners, basis arbitrage, and distressed-event setups. Some funds also trade CDS volatility via options on indices (swaptions), or overlay CDS on equity long/short portfolios as synthetic hedges.

Asset Managers and Pension Funds use CDS more selectively, often in large notional sizes for risk management. Index CDS is the most common tool for hedging portfolio exposure or expressing macro credit views. Some also sell protection on high-grade names to enhance yield, particularly in low-volatility, low-rate environments.

Insurers and Credit Guarantors were once major protection sellers, but their role has become more limited since the 2008 crisis. Today, their activity is more conservative and typically involves capital relief trades or tailored credit risk transfer structures.

Corporates and Sovereign Observers rarely trade CDS directly, but may track spreads as a market-implied signal of creditworthiness or counterparty risk. Their engagement is typically indirect—via treasury teams, IR desks, or macro risk monitors.

CDS in Stress Scenarios

CDS markets may seem technical and peripheral during calm periods, but they move to the centre of the action in moments of stress.

Credit default swaps function as early-warning signals and systemic risk transmission channels. When spreads widen aggressively, it often reflects deteriorating credit conditions. But the feedback loop is circular: the rising cost of protection can amplify fear, restrict funding access, and force position unwinds, exacerbating the very risk it was meant to hedge.

Historical examples:

Global Financial Crisis (2007–2008)

CDS spreads on Bear Stearns and Lehman Brothers widened long before equity markets collapsed. They were some of the earliest public indicators of acute balance sheet fragility.

Eurozone crisis (2011–2012)

Sovereign CDS spreads on Greece, Portugal, and Italy signalled restructuring risk months before formal EU acknowledgement.

Global Pandemic (March 2020)

During the early COVID panic, index CDS (on both IG and HY) gapped wider in illiquid conditions, reflecting growing concern over mass downgrades and corporate solvency.

Default Chains and Counterparty Risk:

In theory, a CDS seller pays out when a credit event occurs. But in large-scale defaults, especially sovereign or clustered HY names, the bigger question becomes, “Can they?”

This was a central issue during the 2008 crisis. AIG’s collapse wasn’t just about housing; it was about the $440bn of CDS protection they’d sold, much of it unhedged and under-collateralised. When margin calls began, AIG couldn’t meet them, triggering a systemic panic. A $180bn government rescue followed, not just to save AIG but to prevent its counterparties from collapsing, too.

CDS isn’t just about credit—it’s also about who stands behind the trade.

Why CDS Matters for Market Structure and Liquidity

CDS markets may appear niche, but are critical to how risk is priced, transferred, and hedged in modern credit markets. Their role goes beyond insurance. They influence how credit spreads behave, how bonds are valued, and how liquidity is distributed in stressed conditions.

Risk Transfer Mechanism:

At their core, CDS contracts separate credit risk from bond ownership. This allows investors to hedge or express views without trading the underlying cash instruments. A portfolio manager can hedge default risk on a high-yield bond book without touching the bonds themselves. Conversely, a trader can go long CDS to express a bearish credit view even if they don’t hold the bonds.

This ability to unbundle risk supports market efficiency and enhances flexibility in managing exposure across books and institutions.

CDS also allows for more nimble expression of views across capital structures. For example, an investor may prefer to hedge credit risk via CDS rather than reduce bond exposure, especially when the bonds are illiquid or tightly held. In some situations, the CDS may represent the only tradable instrument pricing in default risk with real-time transparency.

Price Discovery and Signalling:

CDS spreads are often seen as a purer signal of credit risk than cash bonds. CDS contracts are standardised, centrally cleared (in many cases), and trade with tighter bid-ask spreads, especially in the index space. In volatile markets or around idiosyncratic events (e.g., earnings, downgrades, or restructuring rumours), CDS prices often move faster than bond spreads, providing a leading indicator of credit stress.

This dynamic makes CDS useful not just for risk management, but also for credit surveillance. Traders, portfolio managers, and even corporate treasurers often monitor CDS levels as early warning indicators for counterparty or sector risk.

For this reason, CDS is a critical input in cross-asset pricing models, counterparty risk frameworks, and synthetic credit instruments.

Credit-Bond Basis and Market Arbitrage:

The relationship between CDS spreads and bond spreads, the credit-bond basis, is one of the most widely watched dislocations in credit markets. Arbitrageurs track this closely, stepping in when the two markets diverge. This dynamic enforces pricing discipline and links derivatives to physical credit markets. A negative basis, for instance, can offer hedge funds a way to earn carry while being hedged for credit risk.

In liquid names, this arbitrage helps tether CDS and bond markets together. In stressed scenarios, a breakdown in this basis can signal fractured liquidity or impaired market functioning.

These basis trades are also closely watched by macro funds, who use them to express broader views on market dislocation, central bank credibility, or systemic stress. The magnitude and persistence of basis gaps can reveal where market plumbing is starting to strain.

Liquidity Backstop in Stress:

During periods of bond market dysfunction, especially in lower-rated or high-yield names, CDS often remains more liquid than cash bonds. Investors who struggle to offload bonds during a sell-off may hedge using CDS instead, or trade index CDS as a proxy for exposure. This “flight to derivatives” supports price discovery when underlying bonds are bidless or subject to wide pricing dispersion.

In that sense, CDS markets often act as a last-resort liquidity window. When bond markets lock up, CDS can still print—a critical feature for anyone needing to offload risk in real time.

Subscribe now

Conclusion

Credit default swaps remain among global finance’s most important yet misunderstood corners. To some, they’re just insurance. But in practice, they sit at the heart of how credit risk is transferred, priced, and managed across portfolios and market participants.

Used correctly, CDS contracts offer precision tools for hedging and speculation. They allow investors to separate credit risk from duration, express relative value views, or protect portfolios against systemic events. They also provide critical price signals, helping market participants navigate credit cycles and manage risk through volatility.

They’ve become a vital part of modern market architecture, not just for trading credit but also for managing capital, aligning with regulatory regimes, and structuring balance sheet exposures across institutions.

Whether running a macro book, managing an IG bond fund, or watching counterparty risk at a bank, CDS markets sit close to the core of how credit is expressed and defended.

From bank balance sheets to high-yield ETFs, from structured credit desks to pension funds, CDS markets touch far more of the financial ecosystem than most realise.

We hope you enjoyed this primer and insights on credit default swaps. Please leave a like to show your support. As always, comments and opinions are welcome.

AP

For all the attention that stock markets and interest rates get, the real heartbeat of the global financial system ticks quietly in the background: the repo market.

Short for repurchase agreements, repos are short-term secured loans that underpin daily funding flows across the global financial system. Banks, dealers, money market funds, hedge funds, and insurers all rely on repos to borrow or lend cash against high-quality collateral, typically government bonds. It’s the mechanism that keeps liquidity circulating. Without it, credit markets would seize, central banks would lose control over short-term rates, and liquidity mismatches would surface across everything from derivatives desks to corporate treasuries.

Most of the time, the repo market operates quietly in the background, invisible to most. But in moments of stress (like the “repocalypse” of September 2019 or the crunch of March 2020), it becomes the centre of gravity. When repo markets break, the rest of the system feels it fast.

This primer runs through:

- Repo market basics (mechanics and visualisation)
- Why collateral is everything
- Repo’s role in monetary policy and liquidity transmission
- Historical stress events and market fragility
- Key participants in the ecosystem
- Trading strategies, from balance sheet engineering to funding arbitrage

(If you like FX basis, STIRs, or balance sheet plumbing, you’ll feel right at home. If you don’t, grab a coffee and read it twice. This is the machinery behind the curtain.)

Note: When we refer to the “repo rate”, we’re generally referencing the overnight General Collateral (GC) rate on U.S. Treasury collateral. Bloomberg ticker: USRG1T.

Understanding Repo Markets

At its core, a repurchase agreement (or repo) is a short-term collateralised loan. One party sells securities (typically government bonds) to another, with an agreement to buy them back later at a slightly higher price.

There are two legs to every repo transaction:

- Initial Sale: The seller delivers securities and receives cash.
- Repurchase: The buyer returns the securities and is repaid with a small interest payment, known as the repo rate.

From the cash lender’s perspective, the repo is a secured loan. From the borrower’s side, it’s short-term funding backed by liquid, high-quality collateral.

Repo transactions can be structured in various tenors. The most common are overnight, but term repos and open repos1 are also widely used. These agreements sit across a layered architecture—tri-party, interdealer, and bilateral—each with its own operational structures and risk conventions.

Basic Mechanics

Example: A dealer facing month-end balance sheet constraints enters an overnight repo. To manage its leverage ratio, the dealer pledges $1 billion of US Treasuries as collateral and borrows cash at the GC rate. The next morning, the dealer repurchases the securities at a slightly higher price, having secured short-term funding while temporarily reducing balance sheet usage.

Repo Seller (Borrower):

- Needs $X million in cash overnight.
- Sells $1 billion of Treasuries into the repo market.
- Agrees to repurchase the Treasuries the next day at a pre-agreed price (original notional + accrued interest).
- The implied interest, when annualised, reflects the repo rate.

Example: A corporate treasury team managing excess liquidity allocates capital into overnight repos to preserve flexibility while earning a secured return. The company lends cash to the market and receives US Treasuries as collateral, which are returned the following day.

Repo Buyer (Lender/Reverse Repo):

- Has $1 billion in excess liquidity.
- Lends into the repo market and receives Treasuries as collateral.
- Sells the Treasuries back the next day and is repaid the original cash plus interest.
- The implied interest, annualised, gives the repo rate.

In both cases, collateral is assumed to be US Treasuries. In practice, repo rates vary depending on the quality and scarcity of collateral, the tenor of the trade, and market liquidity conditions.

Subscribe now

Repo Markets Visualised

Our good friend is well known for his infographics, with the below providing a great in-depth view of repo market participants and trade flow routes in the repo space:

Repo Rates Versus Other Types

Understanding how repo rates compare to other short-term interest rates is critical for interpreting funding dynamics. The secured nature of repos sets them apart from unsecured lending rates and gives rise to different benchmarks with varying market relevance.

Fed Funds

The federal funds rate is the benchmark for unsecured overnight lending between US depository institutions. It represents the rate at which banks with surplus reserves lend to banks facing shortfalls, typically on an overnight basis.

By contrast, repo transactions are secured by collateral, most often in the form of US Treasuries. This collateralisation significantly reduces counterparty risk, which is why repo rates tend to price below unsecured benchmarks in times of stability.

While the federal funds target range is set by the Federal Reserve, the effective fed funds rate (EFFR) floats within that corridor and reflects actual transactions. The secured GC repo rate (USRG1T) tracks differently, anchored by the value of collateral and broader liquidity conditions in the Treasury market.

In short, repo markets reflect the cost of secured overnight liquidity. Fed funds reflect unsecured interbank trust. The gap between the two becomes especially telling during stress events or regulatory year-end effects.

SOFR

The Secured Overnight Financing Rate (SOFR) is conceptually closer to the GC repo rate. Both are benchmarks for secured overnight borrowing using Treasury collateral. However, there are key differences.

SOFR is calculated and published by the New York Fed once daily, based on a volume-weighted median of overnight repo transactions across a broad segment of the market, including bilateral and tri-party trades. It is designed as a comprehensive, stable benchmark to replace LIBOR.

The GC repo rate (USRG1T), on the other hand, reflects live, tradeable quotes in the dealer market—updated throughout the day and directly observable on Bloomberg terminals. It represents where cash actually clears in the interdealer tri-party market at any given time.

While SOFR and GC repo typically trade in close alignment, divergences can appear during quarter-ends or episodes of funding strain, where GC repo may spike while SOFR remains more stable due to its averaging methodology.

Think of SOFR as a backwards-looking composite, while GC repo prints live at the point of execution. Both reflect secured funding, but one is a benchmark; the other is a market rate.

Bilateral vs Tri-party Repo

The structure of a repo transaction (whether bilateral or tri-party) shapes everything from margining conventions to the interest rate paid.

Bilateral repo involves a direct agreement between two counterparties. Terms are negotiated on a deal-by-deal basis, allowing for greater flexibility in collateral type, haircut, and maturity. However, this flexibility comes with increased operational and counterparty risk, as each trade must be separately managed and settled.

Tri-party repo, by contrast, introduces a third-party clearing agent (typically BNY) to manage collateral selection, valuation, margining, and settlement. This model dominates the general collateral (GC) space due to its efficiency, scale, and automation.

In normal conditions, tri-party repo typically clears at tighter spreads (lower funding costs) than bilateral repo. This is driven by operational streamlining, deeper liquidity, and the netting benefits of large dealer platforms.

However, when specific collateral is needed, such as a particular off-the-run Treasury, bilateral trades become dominant. These so-called “specials” carry a lower implied yield for the lender and a higher funding cost for the borrower, as participants compete to borrow the same issue.

Put simply, general collateral lives in tri-party. Special collateral lives in bilateral. And the cost of funding adjusts accordingly.

Collateral in Repo Markets

If there’s one concept that sits at the heart of repo pricing, it’s collateral.

In theory, repos are about funding. In practice, they’re about collateral—its quality, availability, and desirability. The type of collateral pledged in a repo not only determines the interest rate but also shapes how trades clear, how risk is managed, and where stress emerges.

Typical Collateral

The repo market runs on a collateral hierarchy.

At the top are government bonds: US Treasuries, German Bunds, and UK Gilts. These are the most liquid and lowest-risk assets available in global markets. If a borrower defaults, these securities can be sold with minimal friction, which is why they attract the tightest repo spreads and lowest haircuts. They’re the closest thing to money without being money.

Next are agency securities, issued by US government-sponsored enterprises (GSEs) like Fannie Mae and Freddie Mac. These instruments carry either implicit or explicit government backing, making them broadly acceptable as repo collateral, but with slightly wider spreads and higher haircuts than Treasuries. Their liquidity profile is strong, but not bulletproof.

Further down the stack are investment-grade corporate bonds. These can be repo’d, but usage is less common and much more selective. The credit risk is idiosyncratic, and the market liquidity is thinner. As a result, haircuts are wider, spreads are higher, and each trade tends to be bespoke.

Collateral eligibility and pricing are always a function of risk appetite, regulation, and market conditions, but this broad hierarchy remains intact across jurisdictions.

The Scarcity Factor

Not all collateral is equal. But sometimes, even among equals, one bond becomes more valuable than the rest.

This is where the concept of a “special” comes in. In repo terms, a bond goes “on special” when demand to borrow it overwhelms supply. This drives its repo rate below the GC rate, sometimes dramatically so.

A negative repo rate means the lender is effectively paying to lend the bond. Why would they do that? Because the borrower needs it badly enough to make it worthwhile, typically to cover a short, meet delivery obligations, or hedge a derivative.

The most common driver of specials is short positioning. If a hedge fund is short the 10-year Treasury future, for example, it may need to borrow the corresponding on-the-run 10-year note to deliver into settlement. When many funds crowd into the same trade, the pressure on that specific bond builds, and the repo rate collapses.

Example: March 2021. The most recently issued 10-year US Treasury note went sharply negative in repo. Hedge funds, betting on higher yields and higher inflation, had built significant short positions. To maintain those positions, they needed to borrow bonds. But quarter-end balance sheet constraints limited supply. Dealers, unwilling to lend freely, withheld inventory, tightening the squeeze. At the extreme, repo lenders were paying borrowers just to get access to the bond.

This dynamic made it more expensive to run short positions and signalled tension in the plumbing, despite no broader credit stress. The specialness eased as market positioning normalised, but it underscored how critical collateral dynamics are to funding stability.

Collateral Terms

When a repo trade is booked, the lender doesn’t fund 100% of the collateral’s face value. Instead, they apply a haircut—a discount that protects against market moves and counterparty default.

The size of the haircut depends on collateral quality:

- US Treasuries (on-the-run): ~1.00-2.00%
- Agency MBS or GSE debt: ~1.00%–5.00%
- Investment-grade corporates: 3.00% and above

Haircuts are a crucial safeguard. If the borrower defaults, the lender needs a buffer to sell the collateral without incurring a loss. In times of market stress, haircuts can be raised, sometimes abruptly, forcing deleveraging and amplifying funding pressure.

Haircuts are also where repo intersects with regulation. Under Basel III, secured funding with high-quality collateral and low haircuts is treated favourably for capital and liquidity metrics. This has led to increased reliance on repo as a preferred form of bank funding.

Why Repo Markets Matter

Because retail investors don’t interact with the repo market, its importance is often overlooked. But make no mistake, this is the engine room of modern finance. Repo markets matter for three fundamental reasons:

Core Source of Short-Term Funding

Banks, dealers, and hedge funds depend on repo to fund bond inventories, deploy leverage, and manage day-to-day liquidity. For dealers, especially, repo enables financing of government securities positions without having to unwind them, preserving market liquidity and facilitating client flows.

It also allows institutions to optimise balance sheet usage. Under Basel III and similar regulatory frameworks, secured funding is treated more favourably than unsecured borrowing. Repo markets, particularly those backed by high-quality collateral, offer balance sheet-efficient access to liquidity.

In normal conditions, repo is deep, liquid, and relatively low-risk. But its role becomes even more vital during periods of stress, when unsecured markets freeze, repo often remains the last functioning funding route.

Anchor for Monetary Policy

Repo markets are one of the primary levers through which central banks transmit monetary policy.

Take the Fed’s policy corridor as an example: by conducting repo operations (injecting liquidity) or reverse repo operations (withdrawing liquidity), the Fed nudges overnight market rates toward its target range.

- In a liquidity squeeze, repos provide reserves, lowering short-term rates.
- In a glut, reverse repos soak up excess cash, lifting rates back toward target.

Repo operations also serve as signalling tools. Adjustments in size, frequency, eligible collateral, or counterparty access can all communicate policy direction. And because repo rates influence a wide swath of short-term funding costs—from bank liabilities to commercial paper—they indirectly shape borrowing costs across the economy.

In effect, the repo rate doesn’t just track monetary policy. It helps enforce it.

Leading Indicator of Financial Stress

The repo market reflects the real-time price of cash and collateral. When tensions build—whether due to liquidity mismatches, balance sheet constraints, or collateral hoarding—repo rates move first.

- A sudden spike in GC repo can signal systemic funding stress.
- A deeply negative special rate might indicate positioning imbalances or collateral scarcity.
- A persistent divergence from central bank target rates suggests a breakdown in transmission.

Repo dislocations are often the canary in the coal mine. In 2008, the withdrawal of repo funding preceded institutional failures. In September 2019, the Fed was forced to re-enter the market as GC rates spiked above 8%. In March 2020, even Treasury repo markets buckled under the strain.

When the repo market breaks, it’s not just a footnote, but often the first crack in the dam.

Notable Stress Events

While the repo market typically operates quietly in the background, it has been a central point of stress in several major financial episodes. Below are three examples where dislocations in repo funding revealed broader fragilities across markets.

September 2019

In September 2019, U.S. overnight repo rates experienced a sudden and sharp spike, from around 2% to over 6% in a single day. Some trades even cleared at higher levels intraday.

The dislocation was driven by a temporary shortfall in cash. A combination of corporate tax payments and large Treasury settlement flows drained reserves from the banking system. At the same time, balance sheet constraints prevented large institutions from stepping in to lend.

The Federal Reserve responded by launching a series of repo operations to restore liquidity. These injections calmed the market quickly, but the episode highlighted how little excess capacity existed in the post-crisis funding system. It also prompted a broader rethink of how reserve levels interact with repo market functioning.

Global Financial Crisis (2008–2009)

During the 2008 financial crisis, stresses in the repo market emerged well before the collapse of major institutions. As concerns about credit risk escalated, repo counterparties began demanding higher haircuts, particularly for lower-quality collateral such as mortgage-backed securities, or refusing to roll repo lines altogether.

Institutions like Bear Stearns and Lehman Brothers, which relied heavily on short-term repo funding to finance large securities inventories, found themselves unable to secure liquidity. The pullback in repo availability contributed to their rapid deterioration.

This episode underscored the repo market’s role as a transmission channel for systemic risk, especially when counterparties become unwilling to accept anything but the highest quality collateral.

March 2020

At the onset of the COVID-19 crisis, the US Treasury market—typically the most liquid in the world—experienced significant stress as investors rushed to raise cash. As Treasury securities were sold en masse, repo markets also came under pressure.

Market participants faced difficulty sourcing funding for even the most liquid collateral. Dislocations appeared in both general collateral and specials markets, and some firms were forced to unwind positions due to tighter margin conditions.

In response, the Federal Reserve reintroduced large-scale repo operations alongside a broader suite of liquidity measures. These efforts helped stabilise short-term funding markets and restore confidence in Treasury market functioning.

Key Participants in Repo Markets

The repo market brings together a wide range of participants, each with distinct roles depending on their funding needs, investment objectives, and access to infrastructure. While banks and non-bank financial institutions dominate in terms of daily activity, central banks play an outsized role in shaping the structure and pricing of the market.

- Banks and broker-dealers are the primary liquidity providers in the repo market. Dealers use repos to fund inventories of government securities, manage balance sheet liquidity, and intermediate between cash providers and borrowers. These desks often sit within broader short-term interest rate trading (STIRT) teams or as part of financing units under fixed income, currencies, and commodities (FICC) divisions.

In tri-party and bilateral repo markets, dealers act as both counterparties and facilitators. On the borrowing side, they finance their own positions or source collateral for client trades. On the lending side, they provide access to repo for institutional cash investors, often matching flows across books to run “matched repo” operations that generate spread income.

- Hedge funds are also active repo borrowers. They use repos primarily to obtain leverage for directional or relative value strategies, particularly in government bond markets. For example, hedge funds executing basis or curve trades often rely on repo financing to take on large bond positions while minimising capital deployment.

Because repo transactions are collateralised, they typically offer hedge funds a cheaper form of leverage than unsecured borrowing or derivatives margin funding. This makes repo a central component in leveraged fixed-income strategies.

- Money market funds (MMFs) are among the largest repo lenders. These funds seek secure, short-term investments, and repos meet both their liquidity and credit quality requirements.

MMFs routinely provide cash to dealers in the tri-party market, receiving high-quality collateral in exchange. This function supports market liquidity and provides a key funding channel for primary dealers.

- Central banks participate in repo markets to implement monetary policy, not to meet funding needs. Through repo and reverse repo operations, central banks manage short-term interest rates and influence liquidity conditions.

Facilities like the Federal Reserve’s Overnight Reverse Repo Program (ON RRP) allow eligible counterparties—primarily MMFs, government-sponsored enterprises (GSEs), and banks—to invest surplus cash securely at the administered rate, which acts as a floor for overnight rates. Conversely, standing repo facilities (SRF) allow banks and dealers to obtain liquidity against government collateral, acting as a ceiling on funding costs.

These operations help central banks steer money market rates within their policy corridors and ensure orderly market functioning during periods of stress.

- Large corporates with substantial cash holdings may access repo markets indirectly, typically via banks or investment in money market funds. While they are not major drivers of daily activity, corporate treasurers occasionally use repo to enhance returns on idle cash while preserving overnight liquidity.

This type of participation is more opportunistic and is generally limited to the most creditworthy counterparties and highly standardised repo structures.

Speculation in Repo Markets

While the repo market is primarily transactional—serving the day-to-day funding and liquidity needs of banks, funds, and dealers—there are instances where it facilitates sophisticated trading strategies. These cases are relatively niche and often involve exploiting dislocations in funding rates or market structure.

Specials Trading

When a specific bond becomes scarce in the market, often due to heavy short interest or its status as a benchmark issue, it may trade at a “special” repo rate, meaning it clears well below the GC rate. This dynamic creates opportunities for repo desks and traders with access to that security.

Example: A trader anticipates that a newly auctioned Treasury note will become “special” due to elevated short demand or benchmark index inclusion. They acquire the bond in the cash market and simultaneously lend it out via a repo agreement. If demand rises and the repo rate on that specific bond falls, the trader benefits from the spread between GC funding and the now-lower special rate, effectively earning a premium for supplying the scarce collateral.

This approach does not involve directional risk on rates or prices. It is purely an attempt to monetise the collateral premium embedded in specials pricing.

Basis Trading

Repo markets also play a supporting role in basis trades, where traders arbitrage the price difference between a cash bond and its corresponding futures contract. This typically involves buying the underlying bond and selling the futures contract short.

To execute the bond leg, traders need to fund the purchase, often done through repo. The repo provides efficient, secured financing and reduces the cost of carry, which is a key component in determining the profitability of the trade.

The repo rate directly impacts the implied basis and can influence trade entry levels. While the strategy itself is not about the repo market per se, successful execution often depends on reliable access to repo funding at known rates.

Conclusion

Repo markets form the foundation of modern market liquidity. They enable institutions to finance positions, manage balance sheets, and transmit monetary policy efficiently. When functioning normally, the system remains largely invisible, facilitating trillions in daily flows with little disruption. But when cracks appear, the effects are felt quickly and broadly, exposing just how critical this market really is.

Whether through stress events, dislocations in collateral, or subtle shifts in repo rate dynamics, the repo market offers a real-time lens into the financial system’s liquidity conditions. For those involved in trading, risk management, or macroeconomic strategy, understanding repo is not optional—it’s foundational.

We hope you enjoyed this primer and insights on repo markets. Please leave a like to show your support. As always, comments and opinions are welcome in the comments.

AP

Contributions from Mark Rogerson, EMEA Head of Rates Products at CME Group.

For decades, global financial markets have relied on the seamless flow of capital across borders. But beneath the surface of currency exchange lies a crucial yet often overlooked mechanism: the cross-currency basis. This spread, which reflects the cost of swapping one currency for another via the short-term funding markets, serves as a barometer of global liquidity, central bank policies, and financial system stress.

At its core, the cross-currency basis represents the difference between the implied interest rate from FX swaps and standard interbank rates. In theory, arbitrage should keep this spread near zero, but in reality, it fluctuates due to market imbalances, funding pressures, and shifts in monetary policy. These distortions create opportunities for sophisticated traders (banks, hedge funds, and asset managers) who exploit the basis for arbitrage, hedging, and macro positioning.

Whether it’s a Japanese institution seeking dollar funding, a European bank hedging its balance sheet exposure, or a hedge fund capitalising on dislocations, cross-currency basis trading is a critical yet complex field. This primer explores the mechanics of cross-currency basis swaps, the forces that drive them, and the trading strategies employed by market participants. Understanding these dynamics isn’t just about theory—it’s about navigating the interconnected world of global finance, where liquidity mismatches and central bank interventions create both risk and opportunity.

This primer runs through:

- Cross-Currency (XCCY) Basis Swaps
- Key Participants in the Market
- Drivers of XCCY Basis Movements
- Liquidity and Market Structure
- Trading Strategies
- Behavioural Aspects and Market Psychology

(If you’re into FX, great. If you love macro plumbing and balance sheet nuance, even better. If none of this makes sense yet, no stress. Read it twice. You’ll get there.)

Subscribe now

Understanding Cross-Currency (XCCY) Basis Swaps

Cross-currency basis swaps (XCCY swaps) are about solving a problem: how do institutions safely and efficiently borrow in one currency while holding assets or funding needs in another?

These swaps are essential plumbing in the global financial system—used daily by banks, insurers, corporates, and sovereigns to manage currency mismatches in funding, hedging, and investing. But while the concept sounds straightforward (swap one currency’s cash flows for another’s), the structure carries important nuances.

Initial Exchange (Notional Swap at Spot):

At the outset of a cross-currency basis swap, the two parties exchange notional principal amounts in their respective currencies. For example, a Japanese bank might lend ¥10 billion and receive $75 million in return (based on the spot USD/JPY rate). This exchange is not just a pricing reference, but a physical currency exchange. Each party takes possession of the other’s currency, which they can use to fund loans, invest, or meet liquidity needs.

This is what distinguishes XCCY swaps from pure FX derivatives like Forward outrights, futures or options. The cash actually moves. It’s real balance sheet funding, not just a bet on exchange rates.

Periodic Interest Payments:

Once the initial notional is exchanged, each side pays interest to the other on the received currency. This is typically done quarterly or semi-annually, with the floating rate linked to local interbank benchmarks1. Each counterparty essentially pays what it would have to pay if it had borrowed the foreign currency outright. But here’s where the “basis” comes in: one leg (usually the non-USD leg) includes a spread, called the cross-currency basis.

This basis is either added to or subtracted from the floating rate. Example: If the EUR/USD basis is –25 bps, the EUR leg pays EURIBOR –0.25% and the USD leg pays straight SOFR.

This negative basis reflects the fact that borrowing USD via the swap is more expensive than borrowing EUR. The negative spread is effectively a “premium” that non-USD institutions pay to access dollar funding in the swap market.

Final Exchange (Reversing the Initial Notional):

At the end of the swap’s term (say 3 months, 1 year, or longer), the original notional amounts are re-exchanged using the same spot FX rate from the contract’s start.

This symmetry is key. It means the transaction is not exposed to FX risk from start to finish, assuming both parties hold to maturity. The floating interest rates may fluctuate, but the principal reverts to its original state, making the swap an efficient funding or hedging tool.

The Cross-Currency Basis:

The basis itself (often quoted in basis points) is a market price that reflects real-world frictions and imbalances. In a perfectly functioning global market, where capital flows freely and there’s no credit or regulatory friction, the basis should be zero. This is the world of covered interest parity (CIP)—a foundational principle of FX markets.

But in practice, the basis is almost never zero, as several factors can weigh in (more on these later). Think of the cross-currency basis as a market barometer—it tells you not just about relative interest rates, but about funding stress, liquidity demand, and institutional preferences across the world.

Below shows the current basis spreads for selected major currencies versus the USD across a range of tenors:

XCCY Basis Swaps In Comparison To Other Derivatives

Versus FX Swaps:

Some might initially get confused when comparing XCCY basis swaps with more traditional FX swaps. Both involve the exchange of notional amounts in different currencies at the start and end of the contract. However, the key distinction lies in how interest exposure is managed. In an FX swap, interest rate differentials are implicitly embedded in the forward FX rate—so while you only exchange principal at the start and end, the relative value of those cash flows effectively hedges the interest differential between the two currencies.

By contrast, cross-currency basis swaps explicitly incorporate periodic interest payments throughout the life of the contract, typically on a quarterly or semi-annual basis. This allows for a more granular hedge of both principal and interest rate exposure, and also reduces counterparty credit risk, since exposure is reset regularly rather than accumulated until maturity. Basis swaps are commonly used for longer-dated funding and hedging, though they are not exclusively limited to long tenors.

In short, if you’re hedging short-term FX risk or forward cash flows, an FX swap may suffice. But for multi-period, full exposure hedging of both currency and interest rate risk, the XCCY basis swap offers a more complete and efficient solution.

Versus IR Swaps:

Standard interest rate swaps (IRS) only let you manage the cash flows within the same currency. Although you swap the principal and the cash flows over the tenor of the contract, you can’t hedge foreign currency risk or enter the contract with differing currency legs. Therefore, if a company wants to hedge FX risk and align cash flows across different currencies, the XCCY basis swap is the best choice.

How To Interpret the Spread

Let’s say you’re a European corporate CFO and want a one-year EUR/USD XCCY basis swap. Data shows that the basis is negative -2.875bps:

What does this mean? Simply put, a European borrower entering into a EUR/USD cross-currency basis swap to obtain dollars will not pay more than the market USD interest rate (SOFR) directly. Instead, they will receive less than the prevailing EUR market rate (e.g., EURIBOR) on the euros they lend into the swap—in this case, 2.875 basis points less. Functionally, this reduced return on the euro leg reflects a premium paid for accessing USD funding via the swap, even though the USD leg pays the standard market rate. While economically similar to paying an extra 2.875bps on the USD borrowing, it’s technically expressed as a discount on the return for the non-USD leg.

A negative basis is quite common today, reflecting the historical trend of demand for USD (more on this later).

Key Participants in Cross-Currency Basis Markets

A range of financial players engage in cross-currency basis trading, each with different objectives:

• Corporations & Multinationals: A part of the market relates to the corporate space. Corporations often find it cheaper to borrow in their home currency, so let’s present a scenario where a U.K. business wins a U.S. project and now needs to fund it for the coming couple of years. It can raise debt in GBP, then enter a XCCY swap for USD for this time period. This provides the USD funding, on which it will pay SOFR + the basis. It’ll receive GBP SONIA for the period, and at maturity, revert the USD back to GBP, where it can look to pay off the debt in local currency.

• Banks & Dealers: Act as intermediaries, providing liquidity in swaps while managing their own funding needs. For example, in the corporate scenario above, a bank desk would take the other side of the trade. Given the OTC nature of these trades, the company would want to deal with one of their main banking partners to facilitate such a deal.

• Hedge Funds & Proprietary Traders: Funds can lower their cost of capital by tapping into cheaper funding markets through cross-currency swaps. They often use these instruments to implement currency-linked arbitrage or funding strategies, taking advantage of small mispricings with leverage. Because the basis swap markets are deep and liquid, particularly in major currency pairs, hedge funds frequently use them for short-term relative value trades or to express macro views. In addition to arbitrage, hedge funds may also take speculative positions on the basis itself. During times of market stress, there is often an acute demand for USD liquidity, which causes the cross-currency basis to move sharply more negative. Funds that anticipate this dynamic may position early, paying the basis ahead of dislocations, or receiving it as a mean-reversion trade once central banks intervene or the stress fades.

• Sovereign Wealth Funds & Pensions: Manage currency exposure on global investment portfolios. Although these funds are less speculative than prop traders or hedge funds, they are still a key part of the market given the notional size traded when trying to hedge.

• Central Banks: Monitor and intervene in FX markets to ensure stability, particularly during crises. For example, at the start of the pandemic the Fed reactivated and expanded its swap lines with major central banks, allowing them to access USD directly from the Fed and lend to their own banks, massively improving dollar liquidity.

Drivers of XCCY Basis Movements

The cross-currency basis is rarely zero and fluctuates based on various factors. To understand some of them, consider the following EUR/USD three-month basis spread showing some drivers of it over the years:

U.S. Tax Repatriation:

In late 2017, there was a sharp blowout in the basis, with it trading down to -100bps before rallying back. The Tax Cuts and Jobs Act (passed in December 2017) allowed U.S. multinationals to repatriate overseas earnings at a lower tax rate. This reduced the incentive to hold USD-denominated assets offshore, leading to a pullback in USD supply from foreign affiliates of U.S. corporates. As a result, non-US banks had a harder time sourcing USD in the short-term funding markets. This created a large premium for USD funding (i.e negative basis), but was something that was resolved swiftly.

Pandemic Outbreak:

There was a massive scramble for dollar liquidity as the COVID-19 crisis exploded into global financial markets in early 2020. As markets tanked in early 2020, there was enormous demand for dollar funding for basically everything. This included covering margin calls, repaying USD-denominated debt, hedging dollar exposures and more.

The basis traded so low that on March 15 and again on March 19, 2020, the Fed reactivated and expanded its swap lines with major central banks. Foreign central bank swap-line borrowing surged. As the below chart shows, swaps went from basically zero to $160 billion on March 19, reaching nearly $450 billion by the end of April:

Year-End Funding Turn:

We flagged up the turn in 2020, but you can equally see it pronounced in several other years. There is a well-known seasonal pattern in global funding markets, where you tend to see increased demand for USD liquidity into year-end.

Part of this relates to balance sheet constraints for dealers, where they reduce risk around year-end to shrink their balance sheets and avoid regulatory capital charges. Banks, in general, look to window dress their balance sheets for regulatory disclosures at year-end, which means they offload risky assets, curtail USD lending, and cut swap exposures. This reduces the supply of USD in FX swap and cross-currency swap markets, making the basis more negative.

On the other hand, non-U.S. banks, along with asset managers and corporates, often need to settle USD liabilities or rebalance books at year-end, creating higher demand for USD and putting the basis lower.

This typically reverses in early January, which is why you can see the swift reversal on the chart.

When we look to summarise the drivers, dollar funding stress is typically the main driver behind large basis swings. Interest rate differentials has a more theoretical impact, with central bank interventions also something to have in mind.

Liquidity and Market Structure

The cross-currency basis market has evolved significantly over the past two decades, with a well-developed and active OTC market extending across the curve, not just in the front-end, but also in intermediate and long-dated maturities, in some cases out to 30 years. These longer-dated swaps are particularly relevant for insurers, pension funds, and corporates managing structural currency exposures on multi-year horizons.

While the majority of trades are still booked over-the-counter (OTC), the appeal of this space lies in the bespoke nature of the contracts. Dealers and clients negotiate specific terms (notional amounts, tenors, currency pairs, reset conventions, and collateral arrangements), making the OTC format well-suited to tailored hedging and funding strategies.

Liquidity is deep in the shorter end of the curve, especially in the 1-month, 3-month, and 1-year tenors for major pairs such as EUR/USD and USD/JPY. While long-dated swaps are widely traded and essential for many institutions, liquidity in times of stress or outside of core currency pairs can pose mark-to-market challenges and wider bid-ask spreads.

Advancements in the market mean that you can book and trade exposure on an exchange. In February 2025, CME Group launched EUR/USD Cross-Currency Basis Futures, a cash-settled contract designed as a transparent and cost-effective tool for trading cross-currency basis exposure.

These futures target the 3-month IMM-dated EUR/USD basis and settle against the CME EUR/USD Cross-Currency Basis Index, calculated daily using prices from SOFR futures, ESTR futures, and EUR/USD FX futures.

These futures offer significant advantages compared to the OTC market. OTC XCCY basis swaps involve physical principal exchanges, bilateral negotiations, and varying credit support annexes, leading to opacity and higher costs from counterparty risk and operational complexity.

In contrast, futures are standardised, centrally cleared, and traded on a transparent exchange, reducing risk and enhancing price discovery through the central limit order book (CLOB). Their cash-settled nature eliminates the need for principal exchanges or managing mandatory break clauses, lowering transaction costs and simplifying execution. By streamlining exposure to the EUR/USD basis without requiring separate FX or interest rate swap bookings, these futures provide a more efficient tool for hedging and speculation.

CME also offer a basis watch tool, whereby those without easy access to a Terminal can easily see the implied basis for different contracts:

Despite the advancements in the swap market, any counterparty that needs to book a bespoke deal will still go to the OTC space, and that’s unlikely to change anytime soon.

Trading Strategies in Cross-Currency Basis Markets

To be clear, hedging activity dominates the swap market, especially in terms of notional volume. However, hedge funds and prop desks can and do speculate using the products, with three main ways:

Basis Arbitrage

Let’s say you’re on a prop desk and are tracking the EUR/USD forward curve, the current SOFR and EURIBOR 3-month rates, and the cross-currency basis spread. You might notice that the negative basis on EUR/USD is kicking out, but the forward curve doesn’t match this move.

In this example scenario, the 3-month contract is -80bps, which you feel is excessive, given that it has deviated significantly from covered interest parity (CIP) and the theoretical FX forward pricing.

In this case, if you could borrow USD close to SOFR, you could enter a XCCY 3-month swap on EUR/USD. On the swap, you’d buy EUR and sell USD.

You’d pay the interest at X% on the EUR borrowings but receive USD funding plus the extra on top as the basis (+80bps). Then at the end of the swap, you’d get back the USD and pay it back.

In theory, if there were an arbitrage opportunity here, you would capture it via the difference in the implied cost of USD funding versus the actual cost of funding from the basis swap.

In the real world, exploiting mismatches between FX forward rates and interest rate differentials by using cross-currency basis swaps is difficult. Usually, we’re talking just a few basis points of difference, with large nationals and leverage needed to profit from this trade meaningfully.

Relative Value Trading

This can be thought of as similar to trading curve flatteners or steepeners on sovereign bond yield curves.

Example: A trader might look at the EUR/USD on a one-year basis relative to the five-year basis. Suppose the one-year basis is –10bps, while the five-year is –30bps. If the trader expects this spread to flatten (i.e. the five-year becomes less negative relative to the one-year), they could take a non-directional trade purely trying to exploit the basis movement. In this case, they would receive on the one-year basis and pay on the five-year basis. Then, if the curve flattens (for example, the one-year moves to –15bps and the five-year moves to –20bps), the trade would be profitable.

You can also trade the basis between different currency pairs using a relative value strategy. For example, the five-year EUR/USD basis currently sits at –4 basis points, while the GBP/USD basis is at –2.5 basis points. If you believe this relationship is mispriced, you can express a view by entering a spread trade between the two. Specifically, if you expect the EUR/USD basis to widen further (i.e., become more negative) relative to GBP/USD, you would receive the EUR/USD basis and pay the GBP/USD basis. If the EUR/USD basis moves from –4bps to –6bps while the GBP/USD basis holds steady, the trade would generate a profit, as the widening EUR/USD leg works in your favour and the GBP/USD leg remains stable.

Macro & Central Bank Policy Plays:

Normally, such plays would often be traded on the currencies outright or via rate futures contracts. However, cross-currency basis swaps can also be used.It does present a clean way to express a view if you believe central bank intervention to boost liquidity is coming, such as during the pandemic. In that case, a view that the Fed would step in to ease USD funding pressures would typically be expressed by paying the basis on the non-USD leg (i.e. receiving USD basis), anticipating that the basis would tighten (become less negative) as dollar liquidity improves.

Conversely, if a trader expects market uncertainty or a surge in demand for USD funding, leading to a wider, more negative basis, they could express this by receiving the basis on the non-USD leg (i.e. paying USD basis), positioning for further USD scarcity.

Framing trades relative to the non-USD leg is the market convention, and helps ensure clarity when discussing directional views on the basis.

Behavioural Aspects and Market Psychology

As in STIR markets, cross-currency basis trading is subject to behavioural biases and market inefficiencies. Below are three we wanted to flag:

Herd Mentality:

During funding crises, traders rush to secure USD liquidity, amplifying moves in the basis. We spoke about this earlier when talking about the pandemic rush in 2020. Corporations, banks and asset managers rushed to obtain dollars to cover margin calls, redeem debt, and secure short-term liquidity. The basis blew out massively beyond what interest rate differentials would suggest, and was simply a stampede of the herd.

The year-end funding turn also turns into a herd mentality bias, as it’s a recurring event. Market participants expect this, and begin pre-emptively rushing to secure USD funding in the basis swap market. Swaps often widen sharply in the weeks before year-end, even if real demand doesn’t justify it.

Overreaction to Policy Announcements:

Market participants may overprice expected central bank actions, creating mean-reverting opportunities. This is something that’s not unique to this market, but rather can be seen in almost all asset classes.

However, the overreaction can be noted because the combination of the swap pricing in line with FX and IR implied differentials means that market fear or greed becomes apparent via arbitrage.

Given the lack of liquidity in some currency pairs and tenors (given the OTC pricing), the overreaction can become even more pronounced in the short term.

Recency Bias:

Recency bias in this market leads investors and traders to anchor spread movements on the most recent basis, assuming that the recent widening/tightening will continue. This serves as a blind bias because part of the price action in this market is based on longer-term trends or macro fundamentals.

The year-end seasonal turn is a classic case, with traders front-running expected moves earlier each year, widening the spread sooner and potentially overshooting. Some hedge in anticipation, even if their actual USD needs are minimal, simply because “it happened last year.”

Conclusion

Cross-currency basis trading sits at the intersection of global funding, interest rate policy, and FX risk management. What may seem like a niche segment of the derivatives market is, in fact, a crucial mechanism underpinning global capital flows. It facilitates borrowing across currencies, enables FX-hedged investment strategies, and acts as a release valve when dollar liquidity tightens, whether due to policy shifts, macro shocks, or regulatory constraints.

While most of the market’s volume is driven by hedging needs—corporates funding overseas operations, insurers and pension funds aligning currency exposures—there remains a thriving ecosystem of relative value and macro traders who monitor the basis for signs of dislocation. From exploiting deviations from covered interest parity to expressing views on central bank liquidity conditions, basis traders must blend quantitative discipline with macro instinct.

What makes the cross-currency basis market particularly compelling is its reflection of deeper structural forces. Regulatory pressures, balance sheet constraints, and monetary policy divergence have all distorted what used to be a clean arbitrage relationship. The basis is no longer just a passive pricing element; it’s a signal of global stress, a cost of capital, and a trading opportunity all at once.

As the market continues to evolve (especially with the rise of centrally cleared futures, expanded swap lines, and a maturing RFR environment), traders and institutions will need to adapt. But the core challenge remains the same: managing liquidity and risk across borders, in a world where funding is never truly frictionless.

In that sense, mastering the cross-currency basis isn’t just about pricing a swap. It’s about understanding the structure of global finance—its incentives, imbalances, and behaviour under pressure.

We hope you found this primer helpful. As always, likes and comments are appreciated. Let us know what topics you’d like covered next.

Contributions from Brian Gierke and CME Group.

For decades, LIBOR served as the backbone of global financial markets, setting benchmark interest rates for everything from corporate loans to complex derivatives. But its downfall—triggered by manipulation scandals and a lack of real transaction data—forced regulators to find a more reliable replacement. Enter SOFR, the Secured Overnight Financing Rate, a benchmark rooted in actual market transactions rather than estimates from banks.

Unlike LIBOR, which reflected unsecured interbank lending rates, SOFR is based on overnight borrowing costs in the U.S. Treasury repurchase (repo) market. This shift eliminated the credit risk embedded in LIBOR and created a nearly risk-free benchmark. However, the transition was far from seamless. Financial markets had to adjust to a backward-looking, collateralised rate rather than a forward-looking, credit-sensitive one. Institutions faced challenges in pricing loans, swaps, and futures, leading to the development of term SOFR and spread adjustments to bridge the gap.

Despite initial hurdles, SOFR has now become the dominant USD benchmark. Futures and options tied to the rate have replaced Eurodollar contracts, and the derivatives market has adapted to its unique characteristics. The transition marked one of the largest shifts in financial history, reshaping how interest rates are traded, hedged, and managed.

SOFR trading is critical to Short-Term Interest Rate (STIR) trading, where traders focus on interest rate derivatives tied to short-term funding rates. The following guide explores SOFR and STIR trading in depth, covering the overview and participants in rate markets, the derivative securities and trading strategies employed across markets.

Subscribe now

Overview of SOFR and STIR Trading

SOFR Basics: A risk-free rate based on overnight Treasury repurchase agreements (repo transactions). Since it is backed by high-quality collateral—U.S. Treasuries—SOFR is considered a nearly risk-free rate, eliminating the credit risk component embedded in LIBOR. This fundamental difference significantly affects how interest rate derivatives, loans, and financial instruments are priced and traded in modern markets.

STIR Markets: Includes futures, options, swaps, and repo transactions tied to SOFR or other short-term rates (e.g., EURIBOR or SONIA, the rate markets for the euro and sterling).

Key Players: STIR markets, including SOFR futures, options, and swaps, attract a diverse range of participants, each with different objectives. These markets provide liquidity, risk management tools, and trading opportunities that appeal to both financial institutions and corporate entities.

- Hedge funds and proprietary trading desks are active in STIR markets for speculative opportunities, arbitrage, and yield enhancement strategies. They trade STIR futures and options to bet on Federal Reserve policy changes, engage in relative value strategies (e.g., SOFR vs. Fed Funds spreads), and capitalise on market dislocations. High-frequency trading firms are also involved, providing liquidity and exploiting short-term inefficiencies.
- Banks use STIR markets primarily for risk management, funding, and market-making. Since they provide financing through repo markets and are exposed to interest rate fluctuations, they use SOFR futures and swaps to hedge their own balance sheet risks.
- Large institutional investors, such as pension funds and asset managers, use STIR derivatives to hedge interest rate risk in their fixed-income portfolios. Since short-term rates influence the broader yield curve, managing exposure to front-end rate movements is crucial for long-term investment strategies.
- Central banks monitor and sometimes intervene in STIR markets as part of monetary policy implementation. They assess liquidity conditions, interest rate expectations, and the effectiveness of policy transmission through the short-term rate complex.

Market Growth: CME Group’s Three-Month SOFR futures are among the most actively traded contracts globally, offering deep liquidity and efficient execution.

Innovations of Interest Rate Trading Over the Past 40 Years

Interest rate trading has undergone significant transformation since the 1980s, shaped by technological advancements, economic events, regulatory changes, and the introduction of new financial instruments. Key innovations include:

Expansion of Interest Rate Derivatives (1980s–Present):

- Futures and Options: Eurodollar futures and Treasury futures/options emerged, enabling hedging and speculation on interest rates.
- Interest Rate Swaps: The swap market expanded, allowing the exchange of fixed-rate and floating-rate debt. Swaptions became vital for hedging and speculating on risks. The transition to SOFR-based swaps followed LIBOR’s phase-out.

Electronic Trading and Market Structure (1990s–Present):

The rise of electronic trading platforms shifted from voice to digital execution. High-frequency trading (HFT) increased liquidity in quiet markets but has led to less liquidity in times of stress, while central clearing of swaps was mandated to minimise counterparty risk. Something that continues to be ongoing—a reduction of the algos in times of stress reduces liquidity. This is relevant to the use and placement of stop-loss trades to manage risk in high-vol regimes, such as data releases or surprise Fed actions.

Impact of Economic Events:

- Events like Black Monday (1987) and the Global Financial Crisis (2008) prompted regulatory changes and innovations in risk management.
- The COVID-19 pandemic led to unprecedented monetary policies, influencing interest rate derivative pricing. From FOMC support with lower yields and increased liquidity as a backdrop to fiscal mistakes in Congress leading to an overly inflationary environment. We are seeing the beginnings of this currently as we shift from Bidenomics to Trump tape bombs. This shift is rather dramatic and will offer both risk and opportunities as the market gets it barring.

New Products and Strategies:

- The shift to SOFR, options, swaps and inflation-linked bonds provided new hedging avenues.
- Algorithmic and quantitative strategies gained traction, with AI and machine learning beginning to be utilised for macro predictions and fixed-income ETFs enhancing access to rate-sensitive assets offering additional tail risk potential. Just as a market maker needs to trade the underlying to hedge the book, ETF managers must do the same.

The Future Outlook:

Interest rate trading will continue evolving with technological advancements, including the potential for tokenisation or blockchain of fixed income and increased automation. Despite these innovations, human behavior remains a crucial factor in trading dynamics, influencing decisions through cognitive biases and market psychology.

SOFR Derivatives and How They Trade

A robust derivatives market has developed around SOFR. These instruments allow traders and institutions to hedge risk, speculate on interest rate movements, and engage in arbitrage strategies. The key SOFR derivatives include futures, swaps, options, and basis trades, each serving a distinct role in managing exposure to rate fluctuations. Liquidity has deepened significantly, particularly in CME-listed SOFR futures, making them the cornerstone of short-term rate trading. Below is a breakdown of the main SOFR-based instruments and how they are used in the market.

SOFR Futures:

- Three-Month SOFR Futures (CME) are the most liquid, used for hedging rate expectations and speculation. Traders often pair SOFR futures with Fed Funds futures to express views on monetary policy.
- Pricing: SOFR futures generally trade as a quote of 100% less the interest rate (taking the IMM Index form). If interest rates were 4.50 %, the futures would be quoted as 95.50. At the time of writing, the Z51 (Dec. 2025) contract is currently 96.3250, indicating a December pricing of rates at 3.675% (see chart below).
- Sizing: 1 basis point = $25. Gains or losses on a contract position are calculated simply by determining the number of interest rate basis points (“bps”) by which the contract price has moved, then multiplying by the value of one bp per contract. As with Eurodollar futures, each basis point of contract interest is worth $25. Thus, SFR contract size is $2,500 x the contract IMM Index.

SOFR Swaps:

- Used to hedge floating-rate exposure or speculate on interest rate movements.
- Standardised Overnight Index Swaps (OIS) allow traders to exchange fixed payments for SOFR-linked floating payments.

SOFR Options:

- Includes caps, floors, and swaptions for hedging or trading rate volatility.
- Volatility traders use options strategies like straddles and strangles when expecting large rate swings.

Basis Trading:

- Arbitraging spreads between SOFR and other rates like Fed Funds or Treasury yields.
- Example: If SOFR trades persistently below EFFR (Effective Federal Funds Rate), a trader might short EFFR futures and go long SOFR futures to capture the spread narrowing.

Volatility in overnight SOFR since Oct/Nov has made the SR1-FF basis market more active than ever.

Trading Strategies in SOFR Markets

SOFR markets, with their deep liquidity and increasing sophistication, offer a variety of trading strategies for market participants. These strategies cater to both speculative traders and those looking to hedge interest rate exposure. Whether betting on future Federal Reserve actions, managing rate curves, exploiting market inefficiencies, or trading volatility, SOFR derivatives provide flexible instruments for numerous approaches. Below are the main trading strategies employed by participants in SOFR markets, each aimed at navigating the complexities of rate expectations and economic shifts.

Directional Trading:

- Taking long or short positions in SOFR futures based on expected Federal Reserve rate changes.
- Example: If the market expects rate cuts, traders might long SOFR futures to benefit from falling short-term rates. This is a fairly straightforward speculative trade expression, however, keep in mind that going long = cuts and short = hikes.

Curve Trading:

- Trading spreads between short-term and long-term SOFR contracts to express views on the yield curve.
- Steepener trades: Long near-term SOFR futures, short longer-dated contracts (expecting a steeper curve).
- Flattener trades: Short near-term SOFR futures, long longer-dated contracts (expecting a flatter curve).

Basis Trading:

- Exploiting differences between SOFR and other short-term rates.
- Example: SOFR vs. Fed Funds Spread Trading – traders hedge funding costs by going long/short these rates when spreads widen or narrow.

Volatility Trading:

- Using SOFR options to trade rate volatility expectations.
- Straddles and Strangles2 allow traders to profit from rate swings without picking a direction.
- Another advanced volatility strategy is gamma3 scalping4, which involves dynamically managing options positions by adjusting the hedge based on changing market conditions.
- Example: If a trader buys a call option on SOFR, and SOFR futures begin to move in favour of the call option (prices rise), the trader will need to sell SOFR futures to maintain a neutral position. Conversely, if SOFR futures move against the position, the trader might buy more SOFR futures. This continual rebalancing allows traders to profit from small movements in the underlying rate while managing their exposure. Gamma scalping can be highly profitable in volatile markets where the underlying rates experience frequent swings, and it provides a systematic way of managing options risk while profiting from volatility. However, it requires constant monitoring and quick adjustments to capture those small gains before they evaporate.

(A similar trade can often be seen in convertible note markets. Please see our previous primer here, and find the “Adjusting the Hedge: Rebalancing the Short Position” section.)

Key Behavioural Insights and Opportunities in STIR Trading

By DCP:

While technological and trading vehicle advancements in interest rate trading are significant, the influence of human psychology across all markets continues to shape market behavior and outcomes. As a new generation of traders learns to manage rate risk in an ever increasingly complex market (after the decades of QE and misguided Fed policy), there remains a robust market to express directional views. When the disconnect isn’t about the data, it was more about the market participants wanting a certain outcome, leading to self-reinforcing speculation, or more simply, you can just do stuff.

Herd Mentality:

Collective psychology often drives market reactions, leading to liquidity crises during panic situations—traditionally expressed as flight to quality or long treasuries as a risk-free safe haven in the short term. This, however, can lead to becoming an “investor” once the stress resolves.

FOMO and Overreactions:

Over-interpretation of central bank signals results in exaggerated market movements, both in indices and along the curve. This offers opportunities for market participants to greatly mis-price the futures and offers one of the better reduced risk trades to fade the herd.

Cognitive Biases:

Traders exhibit biases such as anchoring and recency bias, impacting decision-making.

- Anchoring Bias: Traders often rely too heavily on past rate levels and mindsets, struggling to adjust to new economic realities.
- Recency Bias: Market participants overweight recent events, assuming current trends (inflation, rate hikes) will continue indefinitely, i.e. short term reactions to inflation and jobs data.

Delayed Sentiment Shifts:

Markets struggle to adapt to new realities, reflecting the inherent resistance to change or a disbelief of potential tail outcomes. This is a shift in market dynamics from mean reversion and trend-following strategies to contrarian directional bets as old narratives are laid dead.

Confirmation Bias:

Many traders seek data that supports their pre-existing view, whether dovish or hawkish. I prefer to consider how I am wrong.

Current Paradigm

Markets struggle to accept paradigm shifts—whether moving away from LIBOR, transitioning to SOFR, adjusting to structurally higher rates, following the self-assuring narrative, or failing to spend some brain cells thinking about tail risk and alternative outcomes.

Even policymakers fall into this trap. The Fed itself was slow to recognise inflation persistence in 2021 because they wanted their prior low-rate policies to be justified. You can’t fight the fed but you can fade them. Not many saw the potential for rate hikes to 5.25% out of QE… including Jerome Powell.

A good example of a sentiment shift occurred in early February. Expectations were for 0-25bps of cuts going forward based on sentiment and a strong economy. Yet the Dec 25 SOFR contract couldn’t punch through support and put in a double bottom on a 4-hour chart. Why? Traders didn’t want to commit further into the uncertainty of tariffs all while discounting the harmful potential of tariffs and Trump’s commitment, presenting a contrarian opportunity: Enter a long position and test the waters, add at key levels.

And here we are, with three cuts priced now and the indices tumbling, adding fuel to the fire. “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”—Mark Twain

Conclusion

The transition from LIBOR to SOFR reshaped interest rate markets, replacing a credit-sensitive benchmark with a nearly risk-free one rooted in actual transactions. While this shift eliminated certain risks, it introduced new challenges—such as adapting to a backward-looking rate and navigating liquidity in evolving derivatives markets.

For traders and institutions, SOFR markets now offer deep opportunities in futures, swaps, and options, enabling speculation on Fed policy, hedging rate risk, and capitalising on inefficiencies. Strategies like curve trading, volatility plays, and basis arbitrage have gained prominence, while behavioral factors—herd mentality, bias-driven mispricing, and delayed sentiment shifts—continue to create both risks and opportunities.

Ultimately, success in STIR trading requires both technical expertise and an ability to adapt to market psychology. Whether positioning for policy changes, exploiting structural dislocations, or fading consensus narratives, the key takeaway is clear: understanding the mechanics of SOFR is just the beginning—navigating its market dynamics is where the real edge lies.

We hope you enjoyed this primer and insights on STIR trading. Please leave a like to show your support. As always, comments and opinions are welcome in the comments.

AP and DCP

Convertible bonds are a unique class of hybrid securities that combine features of both debt and equity. They provide investors with the benefits of fixed-income investments while offering the potential for capital appreciation through an embedded option to convert the bonds into shares of the issuing company. This dual nature makes them attractive to various investors, including hedge funds, institutions, and retail traders.

This guide explores convertible bonds in depth, covering their structure, valuation, trading mechanisms, key market participants, and the arbitrage strategies commonly employed in the convertible bond market.

Subscribe now

What Are Convertible Bonds?

Convertible bonds are corporate bonds that give the holder the right to convert the debt into a predetermined number of shares of the issuer’s stock. These bonds are typically issued by companies looking to raise capital while offering lower coupon rates than traditional bonds, as the conversion feature provides an added benefit to investors.

Key Features of Convertible Bonds

- Par Value (Face Value): The amount paid back to the bondholder at maturity if the bond is not converted.
- Coupon Rate: The interest rate paid by the issuer to bondholders.
- Maturity Date: The date when the bond must be repaid if not converted.
- Conversion Ratio: The number of shares the bondholder receives per bond upon conversion.
- Conversion Price: The effective price per share at which the bond converts into equity.
- Call and Put Provisions: Some convertible bonds have call provisions (allowing the issuer to redeem early) or put provisions (giving bondholders the option to sell back the bond).

Let’s talk through what this looks like in real life. Below shows the screengrab for an Alibaba convertible bond, issued in May 2024.

It can be converted until maturity in 2031, with it paying a relatively low coupon of 0.5% but coming with a conversion price of $102.80. As you can note, the stock price is currently $136.89. Using the conversion ratio of 9.72, the cost of conversion would be $102.80 x 9.72 = $1,000.22, whereas the current conversion value is $136.89 x 9.72 = $1330.50.

As converting the bond yields proceeds greater than the cost, it would be profitable for an investor to convert this into BABA shares.

As a note, not all convertible bonds offer a coupon. Strategy (fka MicroStrategy) has been one of the most recent well-known players in this space that have issued zero-coupon convertibles, with the perceived value being all in the equity upside:

How Are Convertible Bonds Valued?

Convertible bonds have two primary sources of value:

1. Bond Value (Debt Component): The present value of the future cash flows (coupon payments and principal repayment).
2. Conversion Value (Equity Component): The value of the bond if it were converted into stock at current market prices.

Valuation Models

- Straight Bond Value: The price of the bond assuming no conversion option.
- Conversion Value: The value of the bond if converted into shares.
- Market Price: The actual trading price, which reflects both bond and conversion values plus market expectations.

The bond’s price depends on the movement of the underlying stock. When the stock price is low, the bond trades like a traditional fixed-income security. When the stock price rises above the conversion price, the bond begins to behave more like an equity instrument.

How to Trade Convertible Bonds

Convertible bonds can be traded in different ways, depending on the investor’s strategy.

Primary and Secondary Market Trading

- Primary Market: Investors can buy newly issued convertible bonds directly from companies.
- Secondary Market: Bonds can be traded in the open market, where prices fluctuate based on interest rates, credit risk, and stock price movements.

Trading Strategies

- Directional Trading: Investors who expect the issuer’s stock to rise may buy convertible bonds as a way to gain upside exposure while limiting downside risk.
- Credit Trading: Investors analyse the creditworthiness of the issuing company and trade based on anticipated credit rating changes.
- Yield Enhancement: Some investors hold convertible bonds primarily for their fixed-income component while benefiting from potential stock appreciation.
- Arbitrage Strategies: Hedge funds and sophisticated investors use convertible bond arbitrage to exploit mispricings.

Who Trades Convertible Bonds?

Convertible bonds attract a diverse range of market participants:

- Institutional: Pension funds, insurance companies, and mutual funds invest in convertible bonds for yield and equity exposure. Hedge funds employ convertible bond arbitrage strategies to take advantage of market inefficiencies. Convertible bond arbitrage is a market-neutral strategy employed by hedge funds and proprietary trading desks. It seeks to exploit mispricings between a convertible bond and the underlying stock.
- Corporates: Companies issue convertible bonds to raise capital while potentially reducing their cost of borrowing.
- Retail: Individual investors who understand the hybrid nature of convertibles may include them in diversified portfolios.

The Mechanics of Convertible Bond Arbitrage

A precursor to the following section: If you want to understand option Greeks or feel a refresher is needed, please visit our previous primer here.

Buying the Convertible Bond:

The arbitrageur purchases a convertible bond issued by a company. This bond has both a fixed-income component (coupon payments, credit protection) and an equity component (the embedded option to convert into stock). If the bond is trading at a discount relative to its theoretical fair value, it presents an arbitrage opportunity.

Shorting the Underlying Stock:

To hedge equity risk, the trader simultaneously shorts the issuer’s stock. This is done in proportion to the delta (Δ) of the convertible bond, which measures the sensitivity of the convertible bond price to changes in the stock price. Delta represents the probability that the bond will be converted into stock. A high delta (close to 100) means the bond behaves more like equity, while a low delta (close to 0) means it behaves more like a traditional bond.

The trade creates a long bond/short stock position that is delta neutral and long equity volatility and long credit of the issuer.

Let’s go back to the BABA Convertible we discussed earlier. Below is the snapshot of the Greeks:

As you can see, the delta is currently above 85, meaning that the ITM nature of it will see it trade more like the equity. This makes sense, given the conversion ratio and conversion price and subsequent share price rally. A quick look at the bond price action confirms that the high delta is causing the bond to trade more like an equity position. Note also the vol of 41.7!

Adjusting the Hedge: Rebalancing the Short Position

One of the key complexities of convertible bond arbitrage is the dynamic nature of the hedge, requiring continuous adjustments to the short position as the stock price moves.

Stock Price Increases → Short More Shares

- As the stock price rises, the convertible bond becomes more valuable because conversion into equity becomes more attractive.
- The bond’s delta increases, meaning it behaves more like equity rather than debt.
- To maintain a neutral position, the arbitrageur must short additional shares to offset the increasing equity exposure.

Stock Price Declines → Cover Some of the Short

- If the stock price drops, the probability of conversion decreases, and the bond starts behaving more like a traditional bond (with a lower delta).
- The trader reduces the short position by buying back some shares since less equity exposure needs to be hedged.

This process, known as dynamic hedging, requires continuous monitoring of delta and frequent rebalancing.

Key Risks in Convertible Bond Arbitrage

While this strategy is designed to be market-neutral, several risks can affect profitability:

- Stock Borrowing Costs: Shorting the stock requires borrowing shares, which can become expensive if demand for the borrow is high or if the stock becomes hard to locate.
- Credit Risk of the Issuer: If the issuing company experiences financial distress, the bond price may decline, and the strategy could suffer losses despite the equity hedge.
- Corporate Actions: Events such as dividends, share buybacks, or takeovers can impact both the stock and the convertible bond, affecting the effectiveness of the hedge.
- Volatility Risk: The effectiveness of the strategy relies on stable volatility levels. A significant drop in volatility can reduce the value of the embedded option within the bond, impacting profitability.

Risks and Challenges in Trading Convertible Bonds

While convertible bonds offer benefits, they also come with risks:

Market Risks

- Equity Risk: If the stock price declines significantly, the bond may trade as a regular fixed-income security.
- Interest Rate Risk: Rising interest rates can lower bond values. The sensitivity of long-dated convertibles can’t be ignored, with some convertibles being very long-dated:

- Credit Risk: A company’s deteriorating creditworthiness can hurt convertible bond prices.

Arbitrage-Specific Risks

- Stock Borrowing Costs: Shorting the underlying stock may become expensive if shares are hard to borrow.
- Liquidity Risk: Some convertible bonds are thinly traded, making execution difficult.
- Corporate Actions: Events like dividends, stock splits, or takeovers can impact arbitrage strategies.

Why Convertible Bonds Attract Funds, Or At Least Used To

Convertible bonds appeal to investors for several reasons:

- Downside Protection: The bond component provides fixed-income security.
- Equity Upside: Investors can benefit from stock price appreciation.
- Yield Advantage: Convertible bonds may offer higher yields than common stock dividends.
- Diversification: They provide exposure to both debt and equity markets.

In the book “Beat the Market,” Dr. Edward Thorp reveals that by employing convertible arbitrage techniques, he achieved annualised returns of 25% with virtually no risk during the years 1961 to 1965.

Looking at more recent performance, between 1990 and 2007, the Hedge Fund Research (HFR) convertible arbitrage index managed to secure annualised returns of 10%, accompanied by an annualised volatility of just 5% based on quarterly returns. However, this performance no longer quite matched the level to beat the market, as the S&P 500 delivered annualised returns of 11%, albeit with a much higher annualised volatility of 15%.

The market crash of 2008 stands out as one of the most significant events in recent financial history. In 2007, more than 81% of fund managers managed to achieve positive returns, with many funds holding steady through the early months of 2008. However, the collapse of Wall Street firm Lehman Brothers had far-reaching consequences. The fallout from Lehman’s troubled shares led to significant devaluations of convertible bonds, among other financial instruments. Additionally, a global ban on short selling left fund managers unable to effectively rebalance their portfolios. To make matters worse, managers faced difficulties selling their underlying securities since potential buyers couldn’t hedge their positions. Ultimately, fund managers saw their portfolios take a nearly 40% hit as a result of the crisis.

The ensuing post-crisis era of zero rates and repressed volatility did little to help convertible arbitrage (and indeed, most other discretionary strategies) replicate pre-crisis investment performance.

That has left the convert arb strategy with a much smaller share of what it used to have.

Sizing of the Market

As of 2024, the global corporate bond market has experienced significant growth, with total issuance reaching a record $8tn. In contrast, the convertible bond market, while also expanding, remains a smaller segment. Global convertible bond issuance in 2024 amounted to $110bn.

That makes the convertible bond market represent just a fraction of the broader corporate bond market, accounting for approximately 1.4% of total corporate bond issuance in 2024.

The surge in corporate bond issuance can be attributed to companies capitalising on strong investor demand. Conversely, the convertible bond market’s growth, though notable, reflects its specialised role in corporate finance strategies and investor portfolios. Namely, Michael Saylor and Strategy were a large issuer of convertible bonds to the market last year.

Conclusion

Convertible bonds are a compelling asset class that blends fixed-income stability with equity upside. They are widely used by institutional investors, hedge funds, and retail traders seeking diversified investment opportunities.

Convertible bond arbitrage is a sophisticated strategy that involves buying a convertible bond while dynamically shorting the underlying stock to hedge equity exposure. The key challenge is adjusting the hedge as the stock price moves—shorting more shares when the price rises and covering shorts when it falls. Proper execution requires access to efficient stock borrowing, robust risk management, and expertise in pricing convertible securities. Despite its complexities, this strategy can provide steady, market-neutral returns when managed correctly.

For those considering convertible bond investing, understanding valuation, market trends, and potential risks is essential to making informed decisions. Whether used for directional bets, income generation, or arbitrage opportunities, convertible bonds offer a unique way to navigate financial markets.

Although subdued since the GFC, there could be a possibility for convertible arbitrage strategies deserving renewed consideration from asset allocators. With the global economy stagnating, convert arb can benefit from a potential new era of increased volatility and significant macroeconomic uncertainty due to trade wars, turbulent yields and increasingly ineffective central bank intervention.

We hope you enjoyed this primer and insights on convertible bonds and the arbitrage strategies related to the securities. Comments and opinions are welcome, as always.

AP

Part of the content that we put out at AP is designed to be educational. Yet we feel there’s also an area where things can be both educational and interesting. The bulk of our readership base is related to global markets in some form but at varying degrees of expertise.

We started a primer series last year for a variety of different financial instruments based on our experience of trading the products. We’ll point out the common mistakes as well as the little wrinkles to make note of, all of which we hope will make users more informed and close the knowledge gap in areas where information isn’t that readily available.

Today, we want to break down the Option Greeks, explaining how each works, which ones are relevant, how to interpret the figures and most importantly - how to use them to enhance decision-making.

Let’s get into it.

Subscribe now

What Are The Option Greeks?

Option Greeks are financial metrics that measure the sensitivity of an option's price to various factors. As such, they are key for anyone who is trading options.

An option is a financial derivative that conveys the right, but not the obligation, to buy or sell an underlying asset at a specified price on or before a specified date.

We’re guessing that if you’re interested in option Greeks, you’ve got a decent base-level knowledge of options in general.

The main Greeks to be aware of are delta, gamma and theta. There are technically a host of other Greeks out there, but they are more for the CFA textbook and aren’t really massively applicable for those using options ‘on the ground’.

For each of the Greeks, we’re going to explain them using a priced-up trade, namely a GBP/USD vanilla call option with a 3-month tenor, spot at 1.2207 and the strike OTM at 1.2600.

Delta

Delta (Δ) is a measure of an option's price sensitivity to changes in the price of the underlying asset. It’s a number that ranges from -1 to +1, with heavy ITM options being closer to -1/+1, ATM options being -/+0.5, and really OTM options having a delta close to 0.

An alternative way of looking at delta is that it can be interpreted as the approximate probability that the option will expire in-the-money (ITM). In reality, this is the main point we use delta for when initially looking at an option pricing.

For example, let’s look at the delta of our GBP/USD option (bottom line):

The second line from the bottom shows the delta at 23.45%. You can convert this from a percentage if you want and look at it as 0.2345 (when using our initial 0-1 banding). So here, the probability that this OTM Call finishes above 1.2600 is just over 23%.

If you wanted, you can specify the option strike based on a desired delta. For example, if you wanted this trade to have a delta of 0.40 / 40%, you could input this, in which case your strike increases to 1.2332. Logically, this makes sense; you’re increasing the likelihood of the option finishes in profit, so the strike is going to be lower than the initial 1.2600.

Why Important

One reason why the delta of a trade is so important is that some traders always target specific strike prices when buying or selling. Let’s be honest: buying an option with a delta of 0 or 100 isn’t going to give you the payoff profile you want in order to generate money in 99% of circumstances.

Therefore, targeting delta in the region of 0.25-0.45 is often seen as the best area. You’re buying/selling OTM strikes, but not a huge way away from the spot price. The probability of it going ITM is reasonable, with better risk/reward parameters.

Now let’s go back to the sign in front of the number that we spoke of to begin with. This is noteworthy as call options have a positive delta of 0-1, whereas put options have a negative delta of 0 to -1.

This is because the delta is the expected change in the option’s price for a one unit change in the price of the underlying asset. So if GBP/USD spot falls, the put options gains. If spot rises, the put value falls. The inverse relationship is why the put delta is negative.

Delta Hedging

Some think that delta hedging is just used by the options desks at large institutions. This isn’t the case, and indeed anyone, even if they just hold one outstanding option position, can make themselves delta neutral (i.e having a delta of zero).

Why would you want to become delta-neutral? Well, if you’re holding a position over a data release, central bank meeting or another event whereby the spot is likely to move, but you don’t want to take on the risk for this event, you could hedge your delta. It would mean that you aren’t exposed to the option price sensitivity to changes in the price of the underlying asset (which is what delta is…)

After things have settled down, you can then remove your hedge if you want.

So let’s say that we bought out GBP/USD option for £1m notional with a delta of POSITIVE 0.23 but wanted to immediately make ourselves delta neutral to bring it down to zero. The ‘hedge’ value shown below gives us the clue:

It gives us a NEGATIVE hedge position of £234k. This should look familiar, given that it’s the same percentage as the delta above it, just converted based on our notional amount. It means that we would sell £234k of the underlying (i.e. a spot trade selling GBP buying USD), which would make us delta-neutral.

Think about it - we’ve bought an option that appreciates in value if GBP gains vs USD. The delta will also increase if the spot moves higher. To ‘hedge’ ourselves against this ‘risk’, we sell GBP at spot, gaining if GBP falls in value vs USD. This gain at spot is needed for the loss in value of the option in this scenario.

The delta is constantly changing, so to be perfectly neutral all the time isn’t that plausible as a retail trader.

Factors Impacting Delta

We’ve mentioned that if the underlying moves more ITM, the delta will increase. Another factor is that as we approach expiration, ITM options will move towards 1, while OTM moves towards 0.

Higher volatility can cause deltas of ATM options to stabilise closer to 0.5 (as the probability distribution of prices widens).

Delta Neutral Structures

Some traders don’t want to be exposed to delta at all. They might reason that they aren’t wanting to bet on a directional movement in the spot rate but rather on another factor, such as volatility.

If a trader wants to bet that GBP/USD will become more volatile over the next three months but doesn’t want to be exposed to delta, they can buy delta-neutral ideas.

For example, look at the straddle below:

In this case, we’ve bought a 1.2404 strike call and a 1.2404 strike put option. The delta is almost 0.5 for the call and -0.5 on the put. So, our net delta is 0. The trader wouldn’t be negatively impacted by spot moving in one direction. Rather, if it was a volatile move (in either direction), the trade would be profitable.

Gamma

Gamma (Γ) is one step further on from delta. Specifically, it measures the rate of change of an option’s delta with respect to changes in the price of the underlying asset.

The first thing to note is that gamma is always positive for both call and put options, as delta increases for calls and decreases for puts as the underlying price moves closer to the strike price.

Let’s go back to our GBP/USD 1.2600 strike Call, with the notional of £1m and the initial delta of £234k. Below, you can see on the top line the gamma exposure:

So, our initial gamma for this OTM strike option is circa £69k. This means that if spot moves 1% higher, the rate of change of our delta would amount to £69k. Put another way, if spot jumped by 1%, we’d need to sell another £69k in order to keep ourselves delta-neutral.

Why Important

Your gamma will be highest at ATM strikes, as the delta is most sensitive to sharp changes as an option moves from either ATM to ITM or ATM to OTM. So, if you are delta hedging, you need to be a lot more active with ATM strikes, as your gamma is going to be high.

Look at the gamma exposure versus spot movements in GBP/USD based on where spot could be at the end of this month:

You’ll note that the gamma is highest if spot was at 1.2600, i.e. ATM. It tails off as the option goes more ITM or OTM.

For heavily OTM or ITM options, gamma is virtually zero. For example, if you changed your strike on GBP/USD to 1.40, you have a delta of zero. Yet even if spot moves a cent higher, the delta is likely to remain zero, given how OTM the strike is. The gamma (the change from 0 to 0) is also zero.

This comes into play primarily when you are delta hedging for risk management. High gamma can lead to significant changes in delta, increasing the risk of large portfolio swings (again with reference to ATM strikes). Understanding gamma allows a trader to better manage their book overall.

Gamma Implications

Another reason to understand gamma relates to the impact it has on the market in general. You might have heard comments such as “the market is long gamma”. Let’s break it down.

If you own options (either calls or puts), you’re long/positive gamma. For example, with our call option, it the market rises, our delta will increase and we will always have a positive gamma figure. This means that we want the market to continue to trend higher.

If you were long a put, your gamma is still positive, and you want the market to continue to trend lower.

On the flipside, if you are selling puts and calls, you’re short gamma. Ideally, you want the opposite to happen; you want the market to not move and just revert back to where it started. This allows you to keep the premium that someone paid to you when you sold the option in the first place. So, being short gamma means you don’t want a market move.

At a more detailed level, some would refer to a trader as being long gamma more specifically if they are purchasing ATM strike options, as this is where the gamma is the highest.

Now, let’s tie it all back together. If the bulk of market participants are option BUYERS, we could say that the market is long gamma. The implication of this is that the asset could experience a period of price stability.

Remember, with the sensitive ATM strikes, a trader who wants to be delta neutral / manage risk that owns the GBP/USD call option would have to sell GBP if it appreciated in value and buy GBP if it fell in value. Now imagine that this effect was magnified by many market participants who are long gamma. This would act to buy any dips and sell any rallies, capping the movement of the pair.

Low Gamma Structures

Based on the above, the combination of a structure that has a leg buying an option and one selling an option would help to make a structure have low gamma exposure.

For example, let’s say that we turned our call option into a call spread, joining our bought 1.26 strike with a sold 1.29 strike. Note the gamma (first line under Greeks) as a result:

In contrast to a positive gamma of £69k, it now is reduced to £28k, thanks to the offsetting impact of selling the call which provides short gamma exposure to the structure.

Technically, if we sold the call at 1.2600, that would make our gamma zero.

With this call spread, it’s not completely gamma neutral as we want the underlying to trend higher to a point, but it does reduce the exposure by a significant degree.

Theta

Theta (Θ) measures the sensitivity of an option's price to the passage of time, also known as time decay. If your brain is slightly scrambled from delta and gamma, this is a rather easier one for us to end on.

It’s logical to assume that the value of an option decreases (all things assumed equal) as it gets closer to expiry. Our 3-month call option has less value than a 12-month one with the same parameters, and part of this is simply down to it having more time for the market to move over a longer period.

So, at a basic level, if you are long options (calls or puts), then you’ll have negative theta because their value diminishes as expiration approaches.

That’s why when we look at our theta exposure on our GBP/USD call, it’s negative:

However, you’ll also notice that it’s small. Each business day, we lose £78.50 in value from the option premium due to the theta decay.

Why Important

Time decay works against you as a buyer of an option. So, at a basic level, purchasing a vanilla option should really only be done if you anticipate a significant move in the underlying asset price BEFORE expiration. It sounds overly simple, but with the rise of retail interest in trading options, it can sometimes get confused with being the same as buying the underlying at spot. It’s not. The finite time of the option means that if you expect a move higher but don’t have a conviction for when it might happen, trade at spot instead of buying an option.

Another key point with theta is to kick out the expiry longer than you expect. Even if we expect a move to happen within a couple of weeks, it’s usually worth kicking out expiry to one month. That way, you’re a little more protected in case things take longer to materialise. Further, if the move does happen within two weeks, you can sell the option back with some time value still on it.

Finally, zero days to expire (0DTE) options should be understood as RISKY. The time decay is rapid, and although these can generate large profits, theta will eat away at the option premium rapidly.

Theta Trading

There are a couple of main ways to try and isolate theta. Firstly, becoming more theta neutral.

Imagine we kept our GBP/USD call, but we turned it into a risk reversal by selling a 1.20 strike put. As we are now selling an option (theta positive), the structure overall becomes theta positive (look at the bottom line):

This means that even though we’ll lose value on the call leg, the premium we net from selling the put will also decrease as time runs out, meaning we’re protected from the time decay.

The other scenario is simply to switch and build an entire strategy around selling options. In this case, theta is your friend, as it erodes the premium that you received. However, the old adage of picking up pennies in front of a steam train can be applicable here. As an option seller, you can open yourself up to potentially unlimited losses should the underlying have a sharp move in one direction. You can reduce this by structures like iron condors, but this removes the direct theta benefit and positions it more as theta neutral, which can defeat the whole point.

We hope you enjoyed this primer on some of the Option Greeks. Comments and opinions are welcome, as always.

Enjoy the weekend.

AlphaPicks

Part of the content that we put out at AP is designed to be educational. Yet we feel there’s also an area where things can be both educational and interesting. The bulk of our readership base is related to global markets in some form, but at varying degrees of expertise.

Based on several requests, we recently starting a primer series for a variety of different financial instruments, based on our experience of trading the products. We’ll point out the common mistakes as well as the little wrinkles to make note of, all of which we hope will make users more informed and close the knowledge gap in areas where information isn’t that readily available.

Our last GPS on FX Options was one of our most liked articles this year, so based on that we’re sticking to FX but looking at spot and forward contracts.

FX Spot is the term used to describe a trade buying one currency and selling another. Technically, it is a contract between the user and the counterparty (i.e a bank) to exchange a specified amount of one currency for another at the spot rate agreed.

A FX Forward is an agreement to exchange currencies at a predetermined rate (the "forward rate") on a future date, beyond the standard spot settlement period. Usually spot trades are done on a T+2 settlement, with forwards in theory being able to go out several years.

Spot and forward can be great ways to express a view in the market, but equally can be used as hedging tools.

As a disclaimer, FX Forwards are complex instruments that have the potential for high losses and should only be traded by experienced investors with a full understanding of the risks involved. The prime does not constitute advice or any recommendations to enter into a specific FX trade.

Subscribe now

FX Spot Basics

Unlike a stock that’s traded on a public exchange, there’s no central market for an FX pair. Rather, market makers from leading banks and brokers put out their own prices for each currency pair.

Given the rise in recent years of eFX (electronic FX), the price arbitrage between different providers is minimal, especially when we’re talking about G10 major currency pairs (for the purpose of this primer we’ll be referring to GBP/USD). The bid is where an investor can sell the base currency (i.e GBP) and the offer is where you can buy the base at.

Below you can see where many market makers are quoting on GBP/USD, and how the pricing is within a couple of pips:

This means that for most retail and institutional investors, it doesn’t matter where you go to book your FX spot trades. The spread to market should be very tight, and although you might not be able to access the interbank rate shown above, most can trade within a few pips of it.

The provider makes money from adding a small spread on top of what the market price is. Even with just a couple of pips, the high frequency nature of FX trades means that this can amount to a large source of revenue over a period of time.

Executing FX Spot

Like most asset classes, FX can be traded either on leverage or without. In our experience, there’s a clear difference when weighing up which to do.

If an investor has good liquidity ($10m+) to allocate to this asset class, then positions can be taken on without leverage. The advantage of executing this way is that the investor can take delivery of the underlying currency and have no risk of being margin called or forced out of the position prematurely. Further, unleveraged FX spot can be used effectively as an overlay into other trades. For example, an investor could be disgruntled by the UK equity prospects for the coming year, as well as thinking that GBP depreciates against USD over the next 12 months.

Therefore, he could sell GBP at spot, buy USD and then take delivery of the USD and invest in US equities. This then becomes a dual-play, ideally gaining from a fall in GBP/USD and gaining from US equity growth. This position could be hedged out from FX risk via an FX forward, but that’s beyond the scope of what we want to cover in the primer.

Alternatively, FX spot can be traded on leverage and this is the most popular option from the retail crowd. However, executing on leverage often comes without knowledge of the size of the notional trade being taken on. Let’s explain.

Let’s assume you have £10k in a trading account, and can access 10:1 leverage on FX. This means that you could have a total notional position size of £100k. Now let’s say that GBP/USD moves 5% against you. The loss is calculated on the notional amount, so £5k. Yet due to the leverage, you’re actually down 50% from your account (£5k relative to £10k).

This isn’t always clear when trading with brokers, mainly because these days you often trade with a size per point / size per pip. In that case, you might see a deal ticket that looks like this:

There are a few things to note here. The size (we have put £1) is the per point monetary movement. The stop loss and limit / take profit levels can also be set by the pip/point.

The initial margin figure can be seen as £56.87. We know from the margin requirements (see below) that this has a deposit factor of 0.45%. This all makes sense, as 56.87 / 0.45% = 12637.

But what about our important notional size? Well this is £12,636. You can work this out by doing £1 x 12636. Or you can figure that if £56.87 is 0.45% of the position size, then 100% is £12,636.

The position sizing on leverage is important, not only to manage your initial margin requirements but also to factor in where you might get margin called.

How To Trade At Spot

There’s a good reason why many say that trading FX spot is the hardest out of various asset classes. To illustrate this, we’ve pulled the forecasts for several major contributors for GBP/USD over the coming year:

You’ll note that BAML are looking for 1.39, while MPS are down at 1.27. There’s a huge range here, reflecting the divergence of views on the Street for the coming year.

What makes FX hard to trade isn’t the execution or the jargon or the pricing, but rather having an accurate macro view of the world. An FX trader is essentially trying to compute the state of one economy (i.e the UK) against the state of another (the US). And not even the current state, but the future state and how each compares. This encompasses politics, economics, monetary policy, natural disasters and everything in between.

We tend to find that trading FX at spot is best served in two ways. The first is intraday, based a lot more on a technical basis and looking for a short-term move. This is suited more for leveraged trading, as the cost of holding the position is small.

The second is with a one-week to three-month time horizon. This is suited more for investors that trade unleveraged and want to take on a view that can encompass future central bank meetings, data points etc. This also allows a wider stop/take profit level.

We see little value in trading FX spot with a horizon greater than three months. It’s too hard to be correct on a view, and even if one wants to express it, it’s much more suited to using Options instead.

FX Forward Basics

Now let’s move onto forwards. A forward can be thought of as a spot trade with a longer settlement period. Yet there’s a key difference in the way these are priced.

The difference is known as the forward points. This is shown below for GBP/USD:

On the far left you have the tenor in yellow. This has SP - spot tenor in blue, which is what someone would trade if they wanted it at spot. Vertically going down you have tenors like 2W (two weeks), 3M (three months), 2Y (two years) etc.

On the next column you have the actual calendar date that the tenor corresponds to. It has to be a working day, so sometimes it reverts to the nearest Friday for settlement.

Then you have the Pts bid / Pts ask. This is the size of the adjustment needed to the market bid/ask to make up the forward. These can be either positive or negative. Positive and you add them to the spot rate, negative and you take them off.

The Fwds bid / Fwds ask is simply the spot added to the points, giving you the outright forward price for buyers/sellers.

The BIG concept to understand on forward pricing is that it does not reflect where the market expects GBP/USD to be at that point of time in the future. Put another way, the 1Y forward price of 1.2611/14 is not a forecast of where the pair will be in a years time.

Rather, the forward points reflect the swap rates of both currencies for the particular tenor. For example, the 1Y GBP/USD forward rate takes into account the GBP 1Y swap rate and the USD 1Y swap rate. The differential then is accounted for as to if the forward rate is above or below the spot rate.

If the base currency has a higher swap rate than the alternate currency, the forward price will be lower than the spot. This reflects the fact that holding the currency with the higher interest rate will earn more interest over the period, so the forward rate needs to be adjusted to factor this in.

With GBP/USD, you can see that the swap rates across different time periods are actually very similar. It’s actually quite rare to have such a minimal difference over longer tenors. For example, in the 1Y tenor, you only have to adjust by 20 pips on the bid to get the forward price.

Trading Forwards

Based on the above, you’ll have probably figured out that trading FX forwards isn’t actually that much about FX, but rather short-term interest rate movements. That’s why these trading desks (STIRT) within major banks mostly sit in the broader FX or FICC teams.

If you want to read an entertaining book about a STIR trader, the below release from earlier this year from Gary Stevenson is worth a holiday read (but take some of his statements around his P/L with a heavy dose of salt…):

Even though the forward points are calculated exactly from the swap rates, the actual swap prices are traded based on a variety of factors, including speculation! If we get some poor data out of the UK, short term Gilt yields, swap rates etc are likely to fall.

So trading an FX forward is partly an expression of trading the differential between two countries interest rate expectations.

That being said, forwards can still be of use to someone who is wanting to speculate on movements in the spot market. For example, for those trading spot on leverage, these positions have to be rolled, which costs. Instead, buying a forward means that there’s flexibility in holding that position for longer than two days. There’s no additional cost of keeping it live/open, as you’ve already priced in the cost of holding the trade via the forward points adjustment at the beginning.

Another way of using forwards is to benefit from leverage. For some, trading at spot on leverage isn’t possible, or isn’t approved on their investment mandate. With a forward, you often only need a small amount of initial margin (2-5%). This can then get you exposure to a larger notional on the forward, which only needs to be settled when the forward ends. You can close out the trade ahead of settlement, meaning only the profit/loss changes hands.

Finally, forwards can be used as an effective hedging tool both for FX positions and cross-asset needs. For example, let’s say that we sold GBP at spot, bought USD and bought Tesla shares. However, I might only want to take on the Tesla risk, not the currency. Therefore, I could sell USD forward for a year to hedge out the GBP/USD price movement.

A side note on this - forward contracts can be rolled, so if I wanted to keep my Tesla shares for longer I could do.

The same principle applies if I’m long GBP/USD just at spot, but am running a profitable position into a central bank meeting, data release or other key event. There are many ways of hedging my spot trade, including using options, but forwards can also be used (i.e in this case selling GBP forward) to eliminate our FX risk for that period.

Primer Summary

FX is a hard asset class to trade profitably over a period of time. Yet being able to understand the basics around the key products, namely spot forward and Options is a great start to avoid making mistakes. Further, understanding when and why to pick a certain product to express a view is key, given the different risk/return profiles.

We hope you’ve enjoyed this Primer (linked to our FXO piece). Please like, share and feel free to comment below.

AlphaPicks

Part of the content that we put out at AP is designed to be educational. Yet we feel there’s also an area where things can be both educational and interesting. The bulk of our readership base is related to global markets in some form, but at varying degrees of expertise.

Based on several requests, we’re starting a primer series for a variety of different financial instruments, based on our experience of trading the products. We’ll point out the common mistakes as well as the little wrinkles to make note of, all of which we hope will make users more informed and close the knowledge gap in areas where information isn’t that readily available.

First up we break down pricing and trading FX Options.

An FX option (FXO) is a derivative placed on underlying foreign exchange (FX) pairs. The FXO gives the holder the right, but not the obligation, to exchange a specified amount of one currency for another at a predetermined exchange rate (the strike price) on or before a specified expiration date.

FXOs are commonly used for hedging against adverse currency movements or for speculative purposes. There are two main types: call options, which give the right to buy a currency, and put options, which give the right to sell a currency. The pricing of FX options depends on factors such as volatility, the interest rate differential between the two currencies, and the time until expiration.

As a disclaimer, FXOs are complex instruments that have the potential for unlimited losses and should only be traded by experienced investors with a full understanding of the risks involved. The prime does not constitute advice or any recommendations to enter into a specific FXO.

Subscribe now

When To Trade Using Options

Options have a different risk/reward profile than trading at spot. This can sometimes make them more attractive to take on a position with.

For example, you might have a view that GBP/USD will head higher over the course of the next few months. Yet when assessing where to place your stop loss and take profit level, a spot trade might only get you a 2:1 RR profile.

However, buying an OTM GBP call might be cheap enough that if the market reaches your target level within the period, you could be looking at a 3:1 or 4:1 RR profile.

Another reason why we like to use FXO in some cases is due to the known upfront loss. This makes the product appealing if we are carrying risk over central bank meetings, tier 1 data releases or other macro risk events. On other products, such as spot, there’s the risk of being slipped on your stop. This means that a fast movement through your stop loss could mean that you get filled at the next available price, which could be significantly different.

This risk doesn’t exist with FXO. Rather, if you are buying a vanilla, you have a known and quantifiable loss via the upfront premium.

Finally, FXO can be a great way to harvest yield. In the low vol environment over the past year in FX, there has been good income to be made from selling options over relatively short tenors and capturing the falling volatility (in the difference between the implied vol which you sell at compared to the realised vol). It’s hard to capture this kind of trade via other products, or at least not as cleanly as can be done here.

How To Price

Below shows the Bloomberg pricing screen, which you can toggle to after loading GBP/USD as a security and then going to the OVML function:

There are a host of different fields that a user can change and ones that need to be understood to correctly end up with the specifications you want. We are going to assume we want to buy a 6-month vanilla GBP call with a 1.3400 strike.

Let’s start from the first line and move selectively down.

Price Date - by default is when you price (we did this yesterday).

Style - a vanilla call is, by default, a European style, meaning it can only be exercised on expiry. When we talk about it being vanilla, it’s a straightforward call or put option with no added features on it.

Direction - you either buy or sell the option, with physical delivery meaning that whatever notional we agree to is the amount of currency that could be bought or sold at the strike level.

Call/Put - don’t forget that a GBP call is the same as a USD put.

Expiry/Delivery - in FX, usually there’s a two day difference between the option expiring and the delivery of funds, although this can be longer if non-working days are included (i.e a weekend).

Strike - we select 1.3400, with the field next to it showing how this strike compares to an at-the-money forward contract. In this case, our strike is higher than the 6 month forward price.

Model - If any of you want to trade using a different model than Black-Scholes, crack on.

Price/Premium - now this is what really interests us. The price of the option (i.e the upfront premium) can be specified in a variety of different ways. Typically we look at it as a percentage of the base currency notional. So in this case, GBP %. From this we can see that the 1.3400 strike call would cost us 1.66% in premium to purchase. Given our notional is £1m, the premium paid is shown underneath of £16.5k.

In this example, if we increased the strike of the Call and kept the other parameters the same, the premium cost would decrease. If we moved it lower, it would increase.

The premium is key as it tells us the kind of move in the spot market we’d need to see in order to breakeven. At a basic level, GBP/USD needs to move to 1.3622 (1.66% above 1.34) in order for this trade to breakeven. If we don’t think that can happen over the next 6 months, this isn’t the right play.

Delta/Hedge - we aren’t going to touch on the Greeks too much today, as they deserve their own primer. Yet noting the delta is important. The delta of an option is the rate of change of the option price relative to a change in the underlying.

Put another way, the delta is the percentage probability at the outset that the option finishes in-the-money. So, from this example, we have a 42.47% probability of success. You can edit this field to solve for the strike, for example, if you wanted to buy a 25-delta strike.

The hedge function shows the £ notional that you would need to use if you wanted to become delta-neutral. So, in our case, we are looking to buy GBP at a strike price of 1.34 and sell it at a higher price. To hedge our delta we would sell GBP at spot to begin with, and sell 42.47% of our GBP notional, £424.7k.

How To Structure

Structuring an FXO usually involves either building multiple options together or using non-vanilla styles. The universe of FXO is extensive and simply serves to capture a specific view of a trader.

For example, in our flagship Monday trade ideas this week, we outlined buying a GBP/USD 2-week straddle structure, which involves buying both a put and a call option at different strike levels for the same expiry date. We felt that the premium for both was actually cheap relative to the risk events over this period (i.e the Fed, BoE meetings). This structure allowed us to not have to take a view on the specific direction of GBP/USD, but rather the volatility of the pair. We didn’t care if it went higher or lower as long as it had a decent move in one direction. As it happens, it did, putting the trade in profit.

Below is another example of how an idea can be structured with FXO, using our earlier example of a vanilla 1.34 strike call. Below, we show the screen with one modification, adding a knock-out feature:

A knock-out is a feature that causes an option to cease to exist if it trades past a certain price level/barrier. So in this case, we change style type to knock-out, which then adds the below fields of barrier direction, type and level.

The direction here is down-and-out, meaning that if the spot market moves down and triggers our lower strike, the option doesn’t exist. The type is American, meaning that it can be triggered at any point during the six month time period. Finally, the level is where we want to place the barrier. We have selected 1.2900.

Now if we go back to the bottom to check out the premium, you’ll note that it has reduced from 1.66% for the vanilla to 1.40% with the knock-out. This is because the feature is a potential negative for the option buyer. If we buy this option and the market briefly touches 1.2900 before rallying to 1.3800, we wouldn’t be able to profit from this as our trade would be knocked out. Therefore, the cost of buying this to begin with is less than the normal vanilla.

If an investor wanted to cheapen the price of a vanilla, adding this feature is one idea. Further, if you didn’t believe the market would go down to the barrier level, adding it might not be a large risk.

Knock outs can also be placed above the strike price. In this case, we would refer to our 1.3400 call option as having an up-and-in feature. Again, this would cheapen the price of the overall structure, as the call would only go live if a higher barrier level, e.g 1.3700, was triggered.

The bottom line with structuring is that there are countless possibilities for building a trade that gives you much more specific details to express a particular view.

Understanding The Smile

When looking at pricing an FXO, the volatility smile is one of the key things we look at. Here is the smile curve in white for the 6-month GBP/USD expiry, alongside the 3-month equivalent in yellow:

First let’s break down what we are looking at here. On the Y axis we have the implied volatility of the option, which is a factor that goes into the price of a FXO.

On the X axis, it shows us the level of delta of the option. In the middle is at-the-money, so basically, a 50-delta strike. As we move to the right, we have Call options (C), moving more and more out-of-the-money. The far right is a 5-delta call, so clearly something with a high strike. The left-hand side is the same style but with puts (P).

Implied volatility generally is higher for OTM strikes. This is driven by investor behaviour and market perception, where the probability of extreme moves (either up or down) is perceived to be higher than what normal distribution would suggest. As a result, the curve of the volatility skew is what gives the diagram it’s name as a smile.

The smile can give us some interesting information. For example, both the 3m and 6m expiry have a skew towards the left. This means that puts are more in demand than calls over these timeframes. This could indicate that investors are cautious about a move lower in GBP/USD. Although this wouldn’t have to change our view (of buying a 1.34 call), it does provide a quick and easy finger on the pulse of where the skew is.

We can also observe that 3m ATM vol is higher than 6m. This is interesting. It could suggest that the market expects higher price fluctuations in the short run due to immediate risks. As such, the implied volatility for the shorter-dated 3m options is higher. The longer-term 6m tenor may have lower implied volatility because you can average out potential volatility over a longer period, reducing the immediate impact of shorter-term events.

For our specific 6m trade idea, we can note that the implied vol is virtually unchanged from ATM strikes through to 25 delta. This means that our 42 delta 1.3400 strike is in the right ballpark for us not to suffer from any spike in implied vol. If there was a spike for whatever reason, observing the smile could allow us to move our strike to a different level.

If you have enjoyed the first iteration of our Primer series, then please leave a like and share this article. We’re always open to feedback to improve our content, so drop us a DM if you have any thoughts you want to share or if there are any particular assets/instruments you want to see featured in the future.