Strategic Risk Management

How can you build portfolios that not only survive crises but actually benefit from them? In Strategic Risk Management: Designing Portfolios and Managing Risk, Campbell R. Harvey, Sandy Rattray, and Otto Van Hemert pose this question to every investor who has watched markets tumble and wondered how to stay afloat. The authors argue that true mastery of portfolio design lies not in chasing alpha during calm markets, but in creating strategies that produce what they call crisis alpha—gains during turbulent market conditions.

This book offers a framework that goes beyond traditional diversification. Harvey and his coauthors challenge the long-standing separation between the investment and risk management functions, asserting that they should be deeply integrated. Instead of treating risk control as a defensive side operation, they propose seeing it as a source of opportunity. Through seven analytically rich chapters, they explore tools like trend following, volatility targeting, strategic rebalancing, and drawdown control—and demonstrate how these quantitative techniques can weave risk management directly into the fabric of investment decisions.

The Problem: Investors Treat Risk as an Afterthought

For decades, finance textbooks and portfolio theory have taught investors to maximize returns given a certain level of risk. But as the authors point out, this has fostered an unhealthy obsession with expected return rather than downside resilience. Typical risk management programs operate like fences—keeping risk contained but isolated from day-to-day strategy. This mechanical approach might work during ordinary times, but when crises strike, investors often discover that they’ve misunderstood their vulnerabilities. Harvey’s team argues that the very structure of portfolio design must anticipate crises as an integral part of its core logic.

What Is Strategic Risk Management?

Strategic risk management integrates defensive measures into the investment process itself. Instead of asking, “How do I protect what I have?”, investors ask, “How do I stay adaptive and capture returns when markets falter?” It requires identifying dynamic strategies with the ability to adjust exposure as conditions change—ones that can act as self-repairing mechanisms. These strategies don’t rely on costly insurance, like buying put options, but aim for favorable convexity: they perform better when volatility rises, and worse when volatility falls. In essence, they replicate the payoff of an option without paying its premium.

The Pillars of the Framework

Across its chapters, the book introduces a coherent system: trend following provides crisis alpha by dynamically adjusting to price movements; volatility targeting stabilizes portfolio risk during turbulent periods; strategic rebalancing prevents mechanical buy-low-sell-high traps; and drawdown control ensures that managers adapt when losses mount. Later, the authors use hedge fund evidence to compare systematic managers (the so-called “machines”) with discretionary ones (“humans”), showing that quantitative systems have risk characteristics surprisingly similar to human intuition when used correctly. Finally, the COVID-19 crash of early 2020 serves as an out-of-sample test that validates their methods—especially the power of volatility scaling and trend-based signals to mitigate large declines.

Why It Matters

If you manage your own investments, you’ve lived through market shocks—dot-com crashes, financial crises, pandemics. Each time, portfolios built solely on diversification often failed because correlations between assets shifted dramatically under stress. Harvey, Rattray, and Van Hemert argue that relying on correlation assumptions is insufficient. Instead, we must analyze how strategies like trend following or quality equity factors behave during drawdowns. These strategies have historically offered positive convexity, meaning they can profit in both bull and bear markets—something traditional allocation can’t deliver. Their data, spanning over fifty years, shows that time-series momentum and quality stock selections can protect capital without the crippling cost of explicit hedging.

A Practical Invitation

The authors don’t present risk management as a dense theoretical exercise but as a set of actionable practices. They encourage readers to test their portfolios under historical crises, simulate drawdowns, and adopt real-time adjustments using volatility and trend indicators. Each idea is accompanied by historical evidence from equities, bonds, commodities, currencies, and hedge funds. By blending empirical rigor with intuitive insights drawn from their decades at Man Group—a prominent hedge fund—they make quantitative risk strategies accessible to both professionals and sophisticated individuals.

Key takeaway

Strategic risk management isn’t about avoiding uncertainty—it’s about designing portfolios that thrive on it. By embedding adaptive mechanisms within investments, you move beyond crisis survival toward consistent resilience. Harvey and his coauthors invite you to stop fearing volatility and start viewing it as an ally.

Over the following key ideas, you’ll learn how time-series momentum generates protective returns during market stress, why volatility targeting transforms how we size exposures, how rebalancing can either harm or help, and how drawdown control reveals true manager skill. You’ll also see how both humans and machines tackle risk in surprisingly similar ways. Each concept builds toward the ultimate insight: that when risk management is strategic, uncertainty ceases to be an enemy—it becomes the source of enduring alpha.

Harvey, Rattray, and Van Hemert open their framework with one provocative idea: some strategies actually benefit from market crises. They call this phenomenon crisis alpha. Instead of scrambling for protection when downturns hit, you can pursue methods that profit precisely when fear dominates. The foundation of crisis alpha lies in trend following—the practice of buying assets with upward momentum and shorting those trending downward.

Trend Following as Synthetic Insurance

The authors demonstrate that a well-designed trend-following strategy behaves like continually owning both a call and a put option—a “long straddle.” During rallies, it buys into winners, echoing the payoff of calls. During crashes, it goes short, similar to having puts. But unlike purchasing options outright, which drains performance in calm markets, trend following adjusts dynamically at low cost. When volatility surges, its convexity shines. Futures on bonds, equities, commodities, and currencies form the testing ground for this idea.

Their empirical evidence spans from 1960 through 2015. What’s striking is that trend following worked not only in equity meltdowns, as conventional wisdom suggests, but also in bond market selloffs. Even during periods of rising yields—such as the inflationary 1970s—momentum strategies delivered protection. The authors designed a representative strategy called momCTA that mimics the managed futures industry benchmark (BTOP50 Index). Its returns were positively skewed, meaning big gains occasionally appeared during turmoil, replicating the economics of an option straddle.

Performance Across Decades and Markets

The research breaks history into two distinct phases: 1960–1985, an era of bond pain and equity volatility, and 1985–2015, the long bond bull run. Trend following succeeded in both. Faster momentum signals—those based on the prior one to four months—performed best, while slower signals lagged. The “crisis smiles” that appeared in their returns plots—where performance peaks during extreme market movements—prove that momentum responds symmetrically to panic and euphoria.

For readers accustomed to thinking of trend as technical analysis, the book reframes it as risk engineering. Momentum is not just a bet on continuation—it’s a method to harvest convexity. When prices move sharply, trend-followers amplify gains instead of letting them dissipate. The authors even show mathematically that these strategies approximate the delta of an option portfolio reacting to volatility spikes.

The Cost-Benefit Equation

Of course, there’s no free lunch. Trend following can lag during choppy, directionless markets—the tradeoff for its crisis performance. Nevertheless, compared to explicit insurance like long puts, it’s vastly cheaper and more flexible. The authors emphasize measuring success not just by annual Sharpe ratios but by its behavior in extremes. In decades of data, momCTA displayed strong returns both in the worst equity months and in the worst bond drawdowns—making it a rare dual hedge.

Why This Matters for You

If you build portfolios for retirement or institutional clients, crisis alpha challenges how you allocate to diversifiers. Rather than buying gold and Treasuries as passive safe havens, consider strategies that act like adaptive insurers. Harvey and his coauthors argue that trend following is the cornerstone of modern defensive investing. It lets you stay engaged in markets without paying the full cost of protection—earning the ability to prosper from distress.

In essence

Trend following transforms reactive defense into proactive opportunity. It’s how disciplined investors adapt in real time, converting volatility—the enemy of most portfolios—into a friend. The lesson is clear: develop strategies that act when fear peaks, not after the storm has passed.

After introducing crisis alpha, the authors ask a question every investor has whispered during turbulent times: Can a portfolio truly be crisis-proof? Their answer is an insightful mix of caution and optimism. Yes, but only at a cost. To explore this, they compare passive defenses—like put options or Treasuries—with dynamic, active hedges such as momentum and quality stock strategies.

Passive Hedging: Expensive Reliability

Buying monthly S&P 500 puts delivers reliable protection. During eight major equity drawdowns between 1985 and 2018—including the tech collapse and Global Financial Crisis—it always worked. The issue is cost: holding puts reduces expected returns dramatically, often producing negative excess returns over long horizons. Bonds, traditionally labeled safe havens, offered mixed results. Their protective power after 2000 was due to unusual negative stock-bond correlations—a historical accident, not a rule.

Safe Havens Under Scrutiny

Gold performed decently in several crises but proved unreliable across centuries (as shown in Erb and Harvey’s earlier research). Its idiosyncratic risks—geopolitical disruptions, mining volatility, and currency linkage—make it unpredictable. Treasury bonds helped in recent recessions yet could fail if correlations revert. The authors show that for most of the twentieth century bonds actually fell when stocks fell—a reminder that yesterday’s hedge may become tomorrow’s hazard.

Active Hedging: Dynamic Protection

Since static insurance drains returns, Harvey’s team explores active approaches that adapt in real time. The first is time-series momentum—adding to winners and cutting losers via futures across fifty markets. Their results show consistent gains during crises and recessions, especially when equity exposure is capped at zero (i.e., cannot go long stocks in crashes). The second is quality stock selection: long high-quality companies and short low-quality ones. Defined by profitability, low leverage, and shareholder-friendly policies, these stocks outperform during panic as investors flee to safety.

When combined, momentum and quality create complementary defenses. They are only modestly correlated, meaning that one may shine when the other lags. Simulation experiments reveal that even modest allocations—say, 30% of portfolio risk—reduce drawdowns by 8 percentage points, transforming catastrophic losses into manageable setbacks. However, full crisis-proof portfolios remain elusive because something must be sacrificed: performance in normal times.

Balancing Cost and Reliability

The authors liken portfolio protection to buying insurance on a house. You can over-insure and lose money slowly or under-insure and lose it all in a fire. It’s about acceptable trade-offs. Dynamic hedges like momentum or quality might fail occasionally—but their long-run returns are positive, unlike options whose guarantees are priced expensively. The task becomes designing portfolios that embed partial protection while maintaining growth.

Key insight

No crisis hedge is free. The most effective defenses—like puts or Treasuries—are also the most expensive. But dynamic strategies demonstrate that with intelligent design, portfolios can absorb shocks without surrendering long-term performance. The goal isn’t perfect protection—it’s resilient participation.

Most investors aim for steady exposure. Harvey, Rattray, and Van Hemert propose the opposite: adjust exposure constantly to maintain stable risk. This idea—called volatility targeting or volatility scaling—reshapes how portfolios respond to market turbulence. Instead of keeping position sizes fixed, you reduce them when volatility rises and expand them when volatility falls, ensuring the portfolio’s risk level stays steady.

Why Volatility Matters

Volatility tends to cluster; quiet months breed calm, stormy months breed chaos. Moreover, risk assets like equities exhibit a leverage effect: their volatility spikes just as prices fall. This combination creates compounding traps—investors endure losses when their holdings are most vulnerable. Volatility targeting flips that dynamic. By scaling down exposure during volatility spikes, it naturally curbs drawdowns. When volatility subsides, scaling up ensures capital remains productive.

Evidence from a Century of Data

Using daily data stretching back to 1926, the authors test volatility-targeted versions of equities, bonds, credit, commodities, and multi-asset portfolios. They find significant improvements: Sharpe ratios for U.S. equities rise from 0.40 to about 0.50; left-tail risk diminishes sharply; and maximum drawdowns shrink by one-third. The “balanced” 60–40 portfolio and “risk parity” portfolios benefit most. Implementing volatility scaling at both the asset and portfolio levels produces higher returns and smoother volatility—crucial for institutional investors with spending requirements.

Hidden Momentum Benefits

Volatility targeting also introduces subtle momentum effects. When equity prices fall and volatility rises, scaling down exposure effectively means selling part of the position—adding an implicit trend-following component. The authors demonstrate that for assets affected by the leverage effect, this built-in momentum improves performance further. In charts plotting return correlations, they reveal that assets with stronger negative return-volatility relationships (like equities and credit) gain the most from volatility targeting.

Practical Application

Volatility targeting is now standard among systematic funds and increasingly common in long-only strategies. Harvey and his team note that risk parity funds epitomize this logic, distributing allocations by risk rather than capital. Investors can apply simple rules: compute recent realized volatility using exponential weighting, set a target (say 10% annual), and adjust position size proportionally. The result is a strategy that self-regulates—more cautious during crises, opportunistic when stability returns.

In short

Volatility targeting turns randomness into rhythm. Instead of being whipsawed by risk fluctuations, you transform volatility from an uncontrollable force into a tool. It’s one of the simplest and most powerful ways to make portfolios resilient.

Rebalancing sounds reassuring—sell high, buy low, maintain balance. Yet, Harvey, Rattray, and Van Hemert disclose a paradox: automatic rebalancing can actually amplify losses. By buying into falling assets during prolonged downturns, investors increase drawdowns. Enter strategic rebalancing—an adaptive rule that times and sizes rebalancing based on market trends.

The Hidden Risk of Mechanical Rules

Imagine managing a 60–40 stock-bond portfolio in 2008. As stocks crash, a monthly rebalance forces you to buy more equities at every trough. The authors quantify the pain: the rebalanced portfolio’s drawdown was about 5 percentage points deeper than a passive buy-and-hold portfolio. That’s because rebalancing resembles selling volatility—similar to writing a short straddle option on the relative performance of stocks and bonds. It profits in range-bound conditions but loses heavily when trends persist.

Countering Negative Convexity with Trend

Their remedy is simple yet powerful: add a small allocation to a trend-following overlay, which has positive convexity. In tests from 1960–2017, combining a rebalanced stock-bond mix with just 10% global trend exposure reduced average drawdowns by 4–6 percentage points—without decreasing returns. Alternatively, investors can modify rebalancing itself: delay trading when momentum signals indicate strong trends. This prevents buying into falling markets or selling into rising ones.

Strategic Rules That Work

Three rules stand out. First, rebalance less frequently—quarterly rather than monthly—to avoid overtrading. Second, rebalance partially, scaling only halfway to target weights. Third, condition rebalancing on trend signals: if the stock-bond trend is negative, postpone. During the 2008 financial crisis and the 2020 pandemic selloff, these rules improved performance materially. The authors’ CEQ (certainty equivalent performance) analysis confirms that strategic rebalancing delivers higher utility-adjusted returns for all reasonable risk-aversion levels.

Lessons for Long-Term Investors

Rebalancing is not simply mechanical. It is a powerful risk lever that must recognize market states. If you manage pension or endowment portfolios, rebalancing strategically means aligning flows and timing adjustments with macro trends rather than calendar dates. As Harvey notes, even Norway’s sovereign wealth fund now channels new inflows toward underweight assets instead of selling winners—an elegant form of strategic rebalancing.

Bottom line

Mechanical rebalancing can increase danger in crises. Strategic rebalancing—guided by trend signals and flexible timing—transforms it into a stability mechanism. Like all their tools, Harvey and his coauthors turn a traditional routine into an adaptive, risk-sensitive strategy.

Every investor fears the gut-wrenching plunge known as a drawdown—the largest peak-to-trough loss in portfolio value. Harvey and his coauthors argue that drawdowns should not just be endured; they should be managed. Their analysis uses simulation studies and manager data to uncover what drives drawdown risk and how to design rules that contain it without killing returns.

The Drawdown Greeks

Like options traders have Greeks for sensitivity—delta, theta, vega—the authors introduce “drawdown Greeks.” The probability of hitting a painful loss depends on three factors: the Sharpe ratio (ability to recover), time horizon (longer evaluation windows give losses more time to compound), and autocorrelation (persistence in returns). Low Sharpe ratios and long horizons make hitting drawdowns almost inevitable. Persistent volatility clustering worsens the odds, explaining why funds that spike risk periodically are fragile.

Replacing or Reducing Risk

The authors model drawdown-based replacement rules for managers. Imagine you oversee several hedge fund strategies. If one loses more than 10%, should you fire them? Paradoxically, firing too fast can eliminate skilled managers during unlucky streaks (Type I error), while waiting too long lets poor ones erode capital (Type II error). They simulate both scenarios and find that drawdown rules outperform simple total-return signals when managers can lose skill over time—a realistic assumption.

For risk reduction rather than outright firing, partial cutbacks can help. Dropping exposure by 50% after hitting a threshold and restoring it once half the recovery occurs preserves capital while maintaining participation. It’s the portfolio equivalent of a safety valve—preventing full-blown blowups while keeping good strategies alive.

Behavioral Implications

Drawdown control shapes manager behavior. Knowing that severe declines trigger redemptions, disciplined managers moderate risk proactively. In that sense, drawdown constraints act like volatility scaling—reducing leverage when volatility spikes. Harvey and team highlight that setting time-varying thresholds, rising as tenure increases, yields smoother replacement rates and fairer evaluation. Many firms use constant limits, but the data suggest adaptive ones are more effective.

Core lesson

Drawdowns are destiny only if you let them be. By using quantitative drawdown rules—cutting risk dynamically and replacing managers intelligently—you can turn the most feared metric in investing into a signal for renewal, not demise.

What’s smarter: human judgment or algorithmic precision? In their study of over 9,000 hedge funds from 1996–2014, Harvey, Rattray, and Van Hemert analyze the performance of systematic (machine-driven) versus discretionary (human-led) managers. Their findings challenge widespread algorithm aversion: systematic funds perform just as well, often better, once adjusted for risk factors.

Understanding Algorithm Aversion

Investors frequently distrust quantitative systems, assuming machines are rigid or prone to failure when conditions change. Behavioral studies (like Dietvorst et al., 2015) show people abandon algorithms after small mistakes. Yet hedge fund data tell a different story. Systematic macro funds earned higher average risk-adjusted returns (around 4.9% annually) than discretionary macro funds (1.6%). In equities, results were similar for both styles once adjusted for factor exposures. Machines are neither universally brilliant nor blind—they’re disciplined executors of risk-conscious strategies.

Factoring Out Simple Exposure

To ensure fairness, the authors strip away performance explained by common factors: market, credit, value, momentum, carry, and volatility. Discretionary funds actually show higher correlations to these factors, meaning their apparent edge often reflects traditional exposures rather than skill. Once factors are removed, systematic and discretionary funds exhibit similar alphas and appraisal ratios. For investors, that means focusing on strategy design and risk controls matters more than whether a human or algorithm executes trades.

Diversification Implications

Both macro and equity hedge fund categories show moderate correlation between systematic and discretionary approaches, enabling diversification. Within each style, the dispersion of returns is enormous—the spread in alpha between top and bottom quartile managers exceeds 10%. That destroys the myth that systematic funds are homogeneous; creativity translates into vast differences in model design and execution. For portfolio allocators, blending styles—human intuition with algorithmic discipline—may capture the best of both worlds.

Main takeaway

Humans and machines are partners, not rivals. The data reveal that systematic risk management produces consistency, while discretionary insight adds adaptability. When combined, they create a portfolio ecosystem that can weather virtually any storm.

The ultimate test of any financial theory is crisis. When the COVID-19 pandemic triggered a sharp global selloff in early 2020, Harvey and his colleagues saw an opportunity: could their strategies hold up outside the sample periods used to develop them? The results were compelling proof that strategic risk management works when it counts most.

Trend Following in Extreme Speed

Stocks fell more than 30% in just over a month—an unusually rapid collapse. Time-series momentum strategies reacted instantly. Shorting equity index futures and adding to bond trends, they logged positive performance while benchmarks like the SG CTA index slightly declined. The reason: most CTAs used slower, moving-average models that responded too gradually, while simpler momentum formulations that Harvey’s team tested were faster. It was a reminder that speed and simplicity beat complexity under stress.

Volatility Targeting’s Triumph

Portfolios that targeted constant volatility slashed their drawdowns nearly in half. By dynamically reducing exposure as volatility spiked, they limited pain while maintaining participation in recovery. Charts in the book show that a volatility-targeted equity portfolio lost about 17%, versus 30% for constant exposure. This evidence reinforced Chapter 3’s findings that volatility scaling provides built-in crisis resilience—a kind of automatic shock absorber.

Rebalancing Under Pandemic Pressure

The authors’ strategic rebalancing rule—rebalance only if trend signals are positive—once again proved useful. During Q1 2020, the rule advised postponing rebalancing in both February and March, when equity trends were sharply negative. As a result, simulated portfolios lost less than mechanically rebalanced ones, confirming that flexibility during crises prevents compounding mistakes.

The Broader Message

COVID-19 underscored a central truth of the book: separating investment and risk management leads to fragility. Integrated approaches—those embedding volatility targeting, trend signals, and adaptive rebalancing—proved to survive and even thrive. The pandemic’s data were an out-of-sample validation, showing that quantitative frameworks built on decades of research can handle the unknown. For investors, it offers confidence that systematic discipline, rather than prediction, creates durability.

Final insight

When the world locked down, strategic risk management scaled up. In crises where human emotion dominates, systems guided by volatility and trend maintain coherence. That’s not just quantitative theory—it’s lived proof that embracing uncertainty yields resilience.