Crisis Alpha Quantified:
How Managed Futures Performed During the Last 3 Global Crashes
An authoritative, 3,000-word econometric analysis charting the exact behavior, positioning, and absolute return profile of Trend-Following CTAs when traditional asset classes collapse.
Introduction: The Elusive Search for Convexity
In the modern era of quantitative finance and institutional portfolio construction, few concepts have captured the imagination of sovereign wealth allocators and pension boards quite like "Crisis Alpha."
Coined by Kathryn Kaminski in her seminal academic work, the term describes a specific, highly coveted return profile: one that exhibits strong positive convexity exactly when traditional risk assets—namely equities, high-yield credit, and real estate—experience severe, cascading drawdowns. Unlike traditional safe-haven assets such as sovereign bonds or physical gold, which rely on fundamental flights to quality or macroeconomic fear, crisis alpha is systematically engineered through divergent risk-taking strategies. The most prominent, scalable, and historically reliable purveyors of this unique return stream are Commodity Trading Advisors (CTAs), particularly those employing systematic, medium- to long-term trend-following methodologies.
The fundamental problem with the ubiquitous 60/40 portfolio is a structural flaw regarding correlation assumption. In times of extreme macroeconomic stress—whether driven by a liquidity freeze, a sovereign debt downgrade, or runaway inflation—the correlation between equities and bonds often converges toward 1.0. We saw the limitations of this paradigm brutally exposed during the 2022 stagflation shock. When market liquidity evaporates and wholesale panic sets in, traditional diversification invariably fails. This is because traditional diversification is inherently linear; it assumes a stationary correlation regime that persists through varying economic climates.
Crisis alpha strategies, however, are explicitly non-linear. They do not seek to predict crises through discretionary macro forecasting; rather, they are structurally designed to adapt to the persistent price dislocations and momentum vectors that crises inevitably produce. By operating across highly liquid, globally diversified futures markets (encompassing global equities, fixed income, foreign exchange, and commodities) and employing strict, mathematically rigorous risk management protocols, CTAs can organically pivot their positioning to align with the dominant macroeconomic regime. Whether that regime is a deflationary bust driving bonds higher, or an inflationary spiral driving commodities higher, the algorithms simply follow the mathematical footprint of human panic and capital flows.
But how well does this elegant theoretical framework hold up against empirical, historical reality? Not all crises are created equal. Some are protracted, fundamentally driven deleveraging events (like the 2008 subprime mortgage collapse), while others are exogenous, hyper-fast liquidity shocks (like the 2020 COVID-19 flash crash). Still others are structural regime changes driven by abrupt monetary policy shifts (like the 2022 Fed hiking cycle). In this massive, exhaustive deep-dive, we will quantify the absolute performance, underlying mechanical drivers, and portfolio integration impacts of Managed Futures across the three most significant global financial crashes of the 21st century. Through granular data analysis, we will demystify the exact conditions under which crisis alpha thrives, the statistical latency involved in capturing trend reversals, and the psychological realities of carrying these strategies during structural, low-volatility bull markets.
The Anatomy of Divergent Risk-Taking
To truly understand how CTAs generate outsized absolute returns during catastrophic market crashes, one must first grasp the underlying mathematics of divergent versus convergent trading behavior. The vast majority of market participants—value investors, yield farmers, statistical arbitrageurs, and mean-reversion quants—operate fundamentally convergent models. They assume that asset prices will eventually revert to a quantifiable intrinsic value. When prices deviate significantly from historical norms, they buy the dip or sell the rip. This strategy generates high win rates and remarkably steady returns during normal, Gaussian market conditions, but it systematically exposes the portfolio to catastrophic left-tail risk when structural breaks occur. Convergent strategies essentially sell unpriced insurance to the market; they collect small, frequent premiums but occasionally suffer massive, unrecoverable payouts.
Trend following, by stark contrast, is explicitly and unapologetically divergent. Divergent traders assume that prices can move infinitely far away from their historical averages due to human behavioral herding, systemic institutional liquidations, or genuine macroeconomic regime changes. When prices break out of established channels, divergent traders follow the movement. They cut losses mechanically and ruthlessly if it proves to be a false breakout (a whipsaw), but they let profits run unhindered if a true, sustained trend emerges.
Mathematically, this methodology creates a return distribution with a conspicuously low win rate (typically hovering between 35% and 45%) but a massively skewed, extremely long right tail. The average winning trade magnitude is often three to four times larger than the average losing trade.
Volatility Scaling: The Engine of Crisis Alpha
A critical, often overlooked component of systematic trend following is inverse volatility scaling. A CTA does not simply buy a fixed number of futures contracts when a trend is identified. Instead, the position size is dynamically calibrated based on the underlying asset's trailing volatility (often measured using Average True Range or standard deviation).
If a market becomes violently volatile (as equity markets do during a crash), the algorithm will systematically reduce the gross position size to maintain a constant target risk level. Conversely, in historically quiet markets like fixed income, position sizes are grossed up significantly. This volatility parity ensures that no single asset class dominates the portfolio's risk profile. During a crisis, volatility clustering causes massive reallocations of risk within the CTA portfolio, allowing it to harvest trends across the commodity and currency sectors while strictly limiting the downside on whipsawing equity shorts.
During a crisis event, markets cease to be randomly walking, normally distributed entities. Price distributions exhibit extreme kurtosis (fat tails). It is entirely within these fat tails that CTAs generate the bulk of their multi-year outperformance. By utilizing moving average crossovers, breakout channels, and volatility-scaled position sizing, a systematic trend follower will automatically short plunging equity indices, buy safe-haven sovereign bonds, or short collapsing emerging market currencies. The beauty of the quantitative model is its absolute macroeconomic agnosticism: it does not require a Ph.D. economist's forecast. It only requires a persistent, identifiable price trend. However, the strict requirement for time-series persistence is the Achilles' heel of the strategy in certain volatile environments. If a crash happens instantaneously without a prior observable trend, the CTA will structurally fail to capture the initial move. This temporal path dependency is critical to analyzing the highly divergent outcomes of 2008, 2020, and 2022.
Return Distribution: Convergent vs Divergent Systematic Strategies
Data Visualization: Hypothetical return distributions illustrating the long right tail generated by strict stop-losses and unbounded profit taking inherent in Managed Futures.
Crash 1: The 2008 Global Financial Crisis
The Archetypal Environment for Trend Following
The 2008 Global Financial Crisis (GFC) stands as the undisputed magnum opus for the Managed Futures industry. For institutional allocators endlessly searching for empirical evidence of crisis alpha, 2008 provided an unequivocal, multi-standard-deviation proof of concept. The GFC was notably not a sudden algorithmic flash crash; it was a slow, agonizing, fundamentally driven unraveling of the global over-leveraged credit system. The underlying rot in the US subprime mortgage market began flashing systemic warning signs in mid-2007. By the time investment bank Bear Stearns collapsed and was forcibly absorbed by JPMorgan in March 2008, a distinct, measurable downward trend in global risk assets had already been firmly established. When Lehman Brothers ultimately filed for bankruptcy in September 2008, triggering a total freeze of global wholesale funding markets and widespread panic, systematic trend-following CTAs were perfectly, algorithmically positioned for the deluge.
Because the 2008 crisis evolved over an agonizingly protracted 18-month period, medium- to long-term mathematical trend models had ample time to ingest the bearish price data, overcome the latency of their trailing moving averages, and definitively flip their signals from long to short.
Positioning Dynamics Throughout the 2008 Crisis
- Equities (Aggressive Short): By the end of Q2 2008, virtually all major commercial trend models had registered definitive short signals on global equity indices, including the S&P 500, Euro Stoxx 50, and the Nikkei 225. As equities cascaded relentlessly lower in Q3 and Q4, CTAs systematically pyramided into these short positions, capitalizing immensely on the immense downward momentum and increasing volatility.
- Fixed Income (Maximum Long): As global central banks, led by the US Federal Reserve, slashed interest rates toward the zero lower bound to combat the catastrophic liquidity freeze, sovereign bonds rallied violently. CTAs rode this massive, historic bull market in US Treasuries, German Bunds, and UK Gilts, compounding their equity short gains with spectacular fixed-income profits.
- Commodities (The Great Reversal): In a fascinating sub-plot, early 2008 saw a massive speculative bubble in crude oil, which peaked dramatically at $147 per barrel in July. Trend followers successfully captured the explosive upside of this bubble in the first half of the year, generating huge returns. Unemotionally, as the commodity complex collapsed alongside the broader real economy in Q3 and Q4, the algorithms flipped short, double-dipping on the unprecedented commodity volatility. This cross-asset flexibility was paramount.
The Hard Performance Data
The performance divergence between traditional assets and trend followers was historically staggering. In calendar year 2008, the S&P 500 Total Return Index plummeted by an agonizing -37.0%. Meanwhile, the SG Trend Index (the industry benchmark tracking the largest institutional trend followers) returned a phenomenal +21.2% net of fees.
This represents a nearly 6000 basis point spread. It was the absolute purest manifestation of crisis alpha: returning remarkably strong positive absolute returns precisely at the moment when the traditional 60/40 portfolio was deeply, systemically underwater.
The 2008 event permanently cemented Managed Futures as a non-negotiable staple in sophisticated institutional alternative buckets. However, it also set a dangerously unrealistic expectation. Naive allocators began to view CTAs as a direct, perfect put option on the S&P 500, fundamentally misunderstanding the requisite time-series path dependency required for the strategy to function. This misunderstanding would lay the groundwork for immense allocator frustration in the quantitative decade that followed.
2008 Performance Divergence: S&P 500 vs SG Trend Index
Data Visualization: The extreme negative correlation achieved during a protracted, fundamentally driven bear market. Notice the smooth acceleration of CTA returns as the equity market collapse deepens.
Crash 2: The 2020 COVID-19 Flash Crash
The Speed Test and the Great Whipsaw
If the 2008 Global Financial Crisis was a perfectly engineered, slow-moving pitch for trend followers to hit out of the park, the 2020 COVID-19 panic was a 100-mph fastball aimed directly at the batter's head. The global pandemic triggered an unprecedented exogenous, biological shock to the highly leveraged global financial system. Unlike 2008, which simmered for long months before finally boiling over, the 2020 crash occurred with terrifying, historically unparalleled velocity.
On February 19, 2020, the S&P 500 was trading at a robust all-time high, oblivious to the impending macroeconomic shutdown. A mere 33 calendar days later, on March 23, the index had crashed by an astonishing 34%. It remains the fastest peak-to-trough bear market in recorded financial history. The CBOE Volatility Index (VIX) exploded from a complacent low teens to over 82 in a matter of weeks, shattering the models of risk-parity funds worldwide.
This sheer, unadulterated speed created a monumental architectural challenge for systematic trend models. Going into mid-February 2020, risk assets had been enjoying a massive, highly robust, low-volatility uptrend. Consequently, almost all medium- to long-term institutional trend models were naturally carrying maximum mathematically permissible long exposure to global equities, coupled with short exposure to safe-haven volatility. When the market violently gapped down due to the exogenous shock, these slow-moving models were caught completely offside.
Because structural trend models inherently rely on historical lookback windows (e.g., 50-day, 100-day, or 200-day moving averages or Donchian channels) to filter out market noise, a sudden, vertical gap down does not instantly trigger a short signal. The price action must break definitively through the moving average and mathematically establish a downward trajectory to confirm a regime shift. By the time the slower, more robust trend models finally flipped from net long to net short in mid-to-late March, the equity market had already absorbed the vast bulk of the catastrophic damage.
And then came the historic, fatal whipsaw: fueled by multi-trillion-dollar fiscal stimulus packages and completely unprecedented monetary intervention by the Federal Reserve, the market executed a perfect V-bottom on March 23 and staged a furious, vertical rally. CTAs, having just established massive short equity positions at the absolute bottom of the market, were immediately forced by their risk engines to cover these shorts at a steep loss as the market ripped violently higher into April.
The Multi-Asset Mitigating Factors
Despite the objectively brutal equity whipsaw, the Managed Futures industry as a whole did not suffer a catastrophic systemic loss in 2020. In fact, many top-tier programs ended the chaotic Q1 2020 with positive absolute returns. How is this mathematically possible? The answer lies strictly in the power of extreme cross-asset diversification.
- The Fixed Income Savior: While equities crashed vertically, global central banks initiated emergency, intra-meeting rate cuts. Sovereign bonds rallied with equal aggression. CTAs, which were already broadly long fixed income entering the crisis due to a grinding multi-year structural bull market in bonds, successfully rode this final explosive rally. This fixed income profit heavily offset their delayed equity losses.
- The Complete Energy Collapse: The unprecedented pandemic lockdowns decimated global travel and economic activity, leading to a total collapse in physical crude oil demand and subsequently, futures prices (culminating in the bizarre, historic negative-pricing event in WTI futures). Because commodity markets were already exhibiting weakness, trend models quickly latched onto this downward spiral, generating massive algorithmic profits on the short side of the energy complex.
The Hard Performance Data
In the highly volatile Q1 2020, the S&P 500 index lost exactly -20.0%. The SG Trend Index, however, managed to grind out a positive gain of roughly +1.5%.
While +1.5% is a far cry from the massive, double-digit absolute returns generated in 2008, it nonetheless provided vital, non-correlated portfolio protection when allocators needed it most. It proved empirically that crisis alpha is not purely reliant on shorting equities; the hyper-diversified, multi-asset nature of CTAs allows them to discover and exploit distinct trends in sovereign bonds, currencies, or commodities even when the equity signal is delayed by unprecedented volatility.
However, the brutal 2020 experience vividly highlighted the distinct vulnerability of slow, long-term trend followers to "V-shaped" recoveries and overwhelming central bank interventions. It sparked a massive wave of research and innovation within the quantitative hedge fund space, directly leading to the widespread integration of faster, short-term trend models and alternative data streams designed specifically to react to rapid volatility expansion.
Crash 3: The 2022 Stagflation Shock
The Death of the 60/40 and the Absolute Triumph of CTAs
If 2020 was an algorithmic speed test, 2022 was a total macroeconomic paradigm shift. For forty years, investors and allocators had been deeply conditioned by the Great Moderation to implicitly believe that sovereign bonds were the ultimate, infallible hedge against equity market declines. The negative correlation between stocks and bonds was the absolute, unquestioned bedrock of Modern Portfolio Theory (MPT). In 2022, under the crushing weight of sticky inflation, that bedrock violently shattered.
Driven by a perfect storm of post-pandemic structural supply chain bottlenecks, massive lingering fiscal stimulus, and the unprecedented geopolitical shock of the Russia-Ukraine war, global inflation indices surged to multi-decade highs. To combat this deeply entrenched, systemic inflation, the Federal Reserve and other major central banks were forced to abandon their dovish posture and embark on the most aggressive, synchronized interest rate hiking cycle in modern financial history. The decadent era of Zero Interest Rate Policy (ZIRP) and quantitative easing was abruptly, ruthlessly terminated.
The result was a catastrophic, highly correlated, simultaneous collapse in both global equities and fixed income. The traditional institutional 60/40 portfolio experienced its absolute worst calendar year since the Great Depression of the 1930s. Traditional asset class diversification offered exactly zero downside protection, as the critical correlation between stocks and bonds flipped viciously positive. There was literally nowhere to hide in the traditional long-only asset universe.
The Perfect Setup for Systematic Trend
For the Managed Futures industry, 2022 presented an extraordinarily ripe, textbook environment. The macroeconomic drivers—runaway inflation and relentless, telegraphed central bank rate hikes—were sticky, persistent, and slow-moving. Unlike the sudden, V-shaped flash crash of 2020, 2022 was a grinding, highly trending, multi-asset bear market. It was an environment precision-engineered for trend-following algorithms.
- The Historic Short Bond Trade: This was arguably the defining macro trade of the decade for quantitative CTAs. As central banks mechanically hiked interest rates, sovereign bond prices plummeted. Because the fundamental macroeconomic driver (inflation) was incredibly stubborn, the downtrend in bonds was exceptionally clean and persistent. Trend models flipped short global fixed income early in the calendar year and relentlessly rode the steep decline, generating historic, career-defining profits.
- Long Commodities (The Inflation Hedge): The pervasive inflation narrative and the sudden war in Eastern Europe sent physical commodity prices soaring, particularly energy, base metals, and agricultural products. CTAs aggressively pyramided into these massive uptrends, providing a direct, highly leveraged algorithmic hedge against the underlying inflation shock.
- Long US Dollar (The Wrecking Ball): As the US Federal Reserve hiked rates significantly faster and harder than its global peers, the US Dollar Index (DXY) embarked on a massive, parabolic rally, acting as a global wrecking ball. CTAs effortlessly captured this macro trend by heavily, systematically shorting the Euro, the Japanese Yen, and the British Pound against the soaring Dollar.
- Short Equities: The steady, grinding, low-volatility nature of the 2022 equity bear market allowed CTAs to cleanly establish and maintain highly profitable short equity positions, further padding their extraordinary returns without the constant threat of violent whipsaws seen in 2020.
The Hard Performance Data
The 2022 performance data is objectively astounding and solidified the strategy's reputation for a new generation of allocators. The S&P 500 finished the agonizing year down roughly -19.4%. Even worse for retirees, the Bloomberg US Aggregate Bond Index crashed by an unprecedented -13.0%. A standard vanilla 60/40 portfolio was utterly decimated, losing over 16%.
In stark, dramatic contrast, the SG Trend Index soared by an unbelievable +27.3%, arguably marking the greatest calendar year in the history of the strategy.
2022 decisively, empirically proved that crisis alpha is not just about hedging equity drawdowns; it is fundamentally about systematically exploiting sustained macroeconomic regime shifts across any liquid asset class. When inflation irreparably broke the traditional stock-bond inverse relationship, CTAs provided literally the only reliable, scalable source of positive absolute return and diversification.
2022 Cross-Asset Returns: The Total Failure of Diversification vs The Success of Trend
Comparative Statistical Analysis Across the Decades
When rigorously analyzing these three disparate historical crash periods, we uncover the deep underlying statistical mechanics of crisis alpha. The efficacy of Managed Futures during any given crisis event is almost entirely dependent on the mathematical signal-to-noise ratio of the specific drawdown event.
| Crisis Event | Macroeconomic Nature of Shock | Drawdown Duration | S&P 500 Return | SG Trend Return | Primary Driver of CTA Alpha |
|---|---|---|---|---|---|
| 2008 GFC | Systemic Credit / Deflationary Bust | ~18 Months | -37.0% | +21.2% | Short Equities, Massive Long Bonds, Short Commodities (H2) |
| 2020 COVID | Exogenous Biological / Liquidity Shock | 1 Month (Hyper-fast) | -20.0% (Q1) | +1.5% (Q1) | Long Bonds, Aggressive Short Energy Complex |
| 2022 Stagflation | Structural Inflation / Rates Shock | ~12 Months | -19.4% | +27.3% | Historic Short Bonds, Long USD, Long Commodities |
The statistical data clearly and repeatedly illustrates that Managed Futures perform optimally during protracted, fundamentally driven macroeconomic regime changes. In 2008 and 2022, the macro environment produced persistent, low-noise price trends that systematic models could effortlessly identify and aggressively exploit. In stark contrast, during 2020, the extreme velocity of the crash and the subsequent, immediate V-shaped central bank reversal created an incredibly high-noise, high-volatility environment where the strategy merely survived and provided marginal protection rather than thriving aggressively.
Furthermore, the critical asset class attribution—which sector generates the actual profits—varies wildly depending on the specific fundamental nature of the crisis. Sophisticated allocators must permanently discard the notion that CTAs are simply equity short-sellers in disguise. In 2008, long sovereign bonds provided a massive tailwind. In 2022, long sovereign bonds were the absolute epicenter of the crisis, and aggressively shorting them generated the bulk of the alpha. This dynamic, unemotional, mathematical flexibility across four major asset classes is the core strategic differentiator between a systematic macro strategy and a simple, decaying equity put option.
The "Bleed" – Understanding the Implicit Cost of Crisis Alpha
In finance, there is no free lunch. No strategy that generates massive, uncorrelated outperformance during catastrophic market crashes does so without exacting a structural cost. For institutional options traders utilizing long-volatility tail-risk funds, the cost of crisis alpha is explicit and painful: the daily, mathematically guaranteed decay of option premium, known in options Greeks as Theta. If the market grinds slowly higher in a low-volatility regime, the put options expire worthless, and the allocator slowly but surely bleeds capital.
For trend-following CTAs, the cost of carrying crisis alpha is not explicit, but rather implicit. It manifests as a frustrating series of small, grinding losses during periods of macroeconomic indecision, rapid mean-reverting whipsaws, or structurally compressed volatility. The long, drawn-out decade between 2011 and 2019 serves as a stark, empirical reminder of this harsh reality.
Colloquially dubbed the "CTA Winter," this nine-year period was characterized by massive, coordinated central bank Quantitative Easing (QE), which artificially suppressed cross-asset volatility and violently forced correlations to converge as liquidity flooded the system. Trend followers suffered through a prolonged, agonizing environment where upside breakouts routinely failed, mean-reversion algorithms dominated the tape, and deep, persistent macroeconomic trends were virtually non-existent.
During a standard, healthy bull market, trend followers generally generate positive, albeit relatively muted, absolute returns simply by remaining systematically long global equities. However, any sudden, sharp equity correction that immediately reverses (the classic "buy the dip" environment fostered by the "Fed Put") will severely punish a CTA. The algorithmic model will be forced to flip short at the exact bottom of the micro-correction to protect capital, only to be forced to cover at a loss as the market inevitably rallies back to new highs. Allocators incorporating Managed Futures must mentally and organizationally prepare for a return profile that distinctly resembles a sawtooth pattern: prolonged, multi-year periods of flat-to-slightly-negative performance, forcefully punctuated by violent, explosive upside during major macroeconomic disruptions.
To effectively mitigate this structural bleed, modern quantitative hedge fund firms have aggressively evolved beyond pure time-series momentum. Today's cutting-edge CTA programs incorporate highly sophisticated alternative data, synthetic markets, alternative trend models (such as term structure carry and macroeconomic seasonality), structural short-volatility yield sleeves, and machine-learning execution algorithms to systematically smooth out the return path between global crises. Nevertheless, the fundamental, inescapable axiom of tail-risk hedging remains universally true: one must be willing to pay a frustrating toll during times of peace in order to ensure you have a fully armed army ready for war.
Strategic Portfolio Integration and Final Thoughts
The vast compendium of historical evidence is empirical and unequivocal: systematic divergent risk-taking, strictly executed by Managed Futures, represents one of the very few academically verifiable and operationally scalable sources of crisis alpha available to institutional and sophisticated retail allocators alike. As we traverse forward into an increasingly fragile, multipolar geopolitical landscape characterized by aggressive deglobalization, highly volatile inflation regimes, and unprecedented sovereign debt burdens, the traditional 60/40 portfolio structure appears not just suboptimal, but profoundly inadequate for fiduciary survival.
Sophisticated allocators should categorically view Managed Futures not as a standalone absolute return vehicle tasked with beating the S&P 500 during a roaring bull market, but rather as critical, structural portfolio insurance that occasionally pays a massive, non-correlated dividend. By systematically reducing total portfolio volatility and significantly muting left-tail drawdowns at the aggregate portfolio level, a prudent 10% to 20% strategic allocation to Managed Futures mathematically improves the overall portfolio Sharpe ratio and drastically curtails the painful recovery time required after a major exogenous market shock.
Whether the next global financial crisis mirrors the slow-burn credit collapse of 2008, the hyper-fast exogenous liquidity shock of 2020, or the grinding stagflationary spiral of 2022, systematic trend-following models are algorithmically, unemotionally designed to relentlessly hunt for the resulting price dislocations. In a financial world fraught with fundamental uncertainty and behavioral panic, relying on the cold, dispassionate, mathematically rigorous laws of price momentum remains a uniquely powerful and historically proven tool for long-term survival and absolute outperformance.
Methodology, Data Integrity & Regulatory Disclosures
This quantitative research analysis relies heavily on the audited historical performance data of the SG Trend Index (formerly known as the Newedge Trend Index). This institutional benchmark is specifically designed to transparently track the daily net-of-fee performance of a curated pool of the largest, most established systematic trend-following commodity trading advisors (CTAs) open to investment. The index is equally weighted and rigorously reconstituted annually to avoid survivorship bias.
All performance figures, drawdowns, and correlation metrics cited herein are for strictly illustrative, academic, and educational purposes only. Past performance is categorically not indicative of future results. The systemic analysis of "crisis alpha" encompasses highly complex econometric modeling, and the specific execution latency, alpha signal generation, volatility scaling limits, and risk management parameters vary heavily among individual quantitative fund managers. Investors should consult a registered financial fiduciary before allocating capital to alternative investment vehicles utilizing leverage and derivatives.
Related Research & Articles
Continue reading about institutional finance and quantitative strategies.
The Ultimate Hedge Fund Statistics Guide (2026)
A massive, constantly updated compendium of over 50 institutional data points covering AUM growth, leverage multiples, and fee structures.
The 100x Club: Analyzing the Extreme Leverage of Relative Value Hedge Funds
A data-viz heavy deep dive into the Repo market, showing exactly how Treasury Basis trades are financed using extreme leverage.
The Shadow Banking Black Box: Cleared vs Uncleared Derivatives
Exposing the systemic risk shifting away from central clearinghouses back into bilateral OTC swaps.
Enjoyed this analysis? Share it with your network.