Beyond the Calm: Understanding Hidden Risks in Market Stability

Introduction: “The Illusion of Safety in Prolonged Calm”

“Risk isn’t about predictability. Risk is about Vulnerability” – Nassim Nicholas Taleb

Imagine sailing on a calm, glassy lake, where the stillness seems to guarantee safe passage. In financial markets, prolonged periods of stability evoke a similar sense of security. However, beneath this tranquil surface lies a buildup of “warehoused risk”—accumulating vulnerabilities that traditional risk models overlook.

Most financial models, including Value at Risk (VaR) and backtesting, use historical volatility as a measure of future risk. These backward-looking approaches assume that the future will mirror the past, obscuring the risks that don’t align with prior data. But risk doesn’t conform to past trends; it compounds silently, amassing potential for disruptive events that evade conventional metrics.

In markets, just as in nature, stability can conceal the unseen forces gathering strength beneath the surface. Our greatest vulnerability isn’t volatility but uncertainty—the unknown risks that arise not from what has happened but from what could happen. In prolonged calm, these risks compound unnoticed, much like snow gathering on a mountainside or dry brush accumulating in a forest.

This discussion will explore how such hidden risks build silently over time and why resilience—not prediction—must be at the heart of sound investment strategies.

The Calm Before the Storm: Why Traditional Models Fall Short

In finance, risk is often gauged through models like Value at Risk (VaR), which estimates potential losses over a set timeframe. For instance, a VaR model might suggest a 95% probability that losses will not exceed a certain threshold, offering a sense of control. However, this perceived control can be deceptive. VaR relies heavily on historical data, assuming the past will accurately reflect the future. In calm markets, VaR can grossly underestimate risk, failing to capture events that lie outside the recent record.

Backtesting, another widely used tool, reinforces this illusion of predictability. By simulating a trading strategy using historical data, it suggests a pathway to consistent returns. However, in the absence of recent shocks, backtests encourage a false sense of security, often overlooking the hidden risks that accumulate in quiet markets, waiting for an unforeseen catalyst.

Even the Kelly Criterion—a popular position-sizing formula designed to maximize long-term growth—relies on past returns and probabilities. It works by calculating the “optimal” bet size based on historical outcomes. In periods of calm, the Kelly Criterion can dangerously encourage larger position sizes, assuming minimal risk. But if an unexpected market shock occurs, these larger positions can amplify losses, exposing investors to potentially catastrophic outcomes. Its formula is:

This approach is highly sensitive to assumptions about probability and payoff. When these assumptions are based on backtesting, they inherently assume that past returns and risk conditions will continue. In periods of prolonged calm, the Kelly Criterion can dangerously encourage larger position sizes, as the model believes risk is minimal. This is where the Kelly Criterion becomes treacherous. If an unexpected market shock occurs—a “black swan” event—the larger position sizes can lead to catastrophic losses.

In this way, VaR, backtesting, and the Kelly Criterion all share a common blind spot: they are backward-looking, designed around the assumption that the future will resemble the past. They can’t account for the unseen risk that builds during market calm, similar to the way snow quietly accumulates before an avalanche or dry brush collects before a wildfire. These tools lead investors to feel secure when the real risks are hidden beneath the surface, gathering strength until they finally erupt.

“Markets, like nature, have cycles and pressure points that aren’t visible until it’s too late.” Traditional models fail to capture this buildup of hidden risks, leading to a false sense of control. A resilient approach to risk management requires understanding the limits of these models and accounting for unseen pressures.

The Avalanche Analogy: Compounding Risk Over Time

Imagine a snow-covered mountain. Each day, a few more snowflakes settle on the slope, small and seemingly harmless. To an observer, each new layer of snow appears insignificant. But beneath this peaceful exterior, the snowpack gradually compresses into a dense, precarious structure. All it takes is one last snowflake or a minor disturbance to trigger an avalanche, unleashing a forceful cascade as the built-up pressure finally releases.

In financial markets, a similar process unfolds. During extended periods of stability, seemingly minor risks—such as slight increases in leverage, shifts in asset concentration, or repetitive risk-taking—accumulate quietly, building up hidden fragility. Like the snowflakes on a mountain, these incremental changes seem harmless. Yet, over time, they form a structure of risk beneath the surface, making markets increasingly vulnerable to a shock that could set off a dramatic chain reaction. This accumulation of risk is often masked by the appearance of stability, creating a deceptive sense of security.

Traditional risk models like Value at Risk (VaR) and position-sizing strategies such as the Kelly Criterion inadvertently encourage this build-up. In stable times, these models suggest that larger, bolder positions are safe, as recent history shows minimal volatility. Investors, lulled into a false sense of security, assume the market’s resilience and expand their exposure. However, it’s precisely during these calm periods that the riskiest structures are formed, much like the quiet layering of snow on a mountain.

This hidden accumulation of risk also extends beyond markets and into the insurance industry, which faces a similar challenge when calculating premiums. Actuaries estimate risk using back tested models based on historical claims data, assigning probabilities to future events based on past patterns. These models, though sophisticated, share the same blind spot as financial backtests: they underestimate risks that have not yet occurred. This approach often leads to insurance premiums that feel like safeguards but may not fully compensate for future, unanticipated risks. Just as financial models often miss signs of market fragility, actuarial models in insurance fail to capture the potential impact of “outlier” events—those that can have devastating, long-term effects on wealth but fall outside standard predictive patterns.

When rare, high-impact events do occur, they can overwhelm both investors and insurers who believed their models sufficiently accounted for risk. Events such as financial crashes, pandemics, and natural disasters have a way of challenging these backward-looking models, pushing losses far beyond anticipated limits. While premiums give a sense of protection, there’s no certainty they’ll cover the full extent of unexpected risks.

“In markets, as in nature, an accumulation of small risks can lead to large, unexpected collapses.” Standard risk assessments fail to account for these compounding vulnerabilities, exposing investors to the avalanche of risks that no model or premium can predict.

The Earthquake Analogy: Underlying Pressure Without Warning

Beneath the earth’s surface, tectonic plates are constantly shifting, creating tension along fault lines. Each minor movement adds a little more stress to these already strained boundaries, and while the pressure builds invisibly, it inches closer to a critical point. Though the surface might appear stable, this hidden stress increases over time until one small shift, often the result of an otherwise minor tremor, releases the full force of an earthquake. When this pressure finally gives way, it can trigger devastating effects, shaking even the most stable ground.

In financial markets, a similar build-up of stress occurs over time, often in the form of hidden fragilities like liquidity bottlenecks, excessive borrowing, or asset concentration in specific sectors. These underlying tensions remain unnoticed during periods of apparent stability. As conditions feel calm, institutions take on more risk, borrowing to increase returns or doubling down on specific assets. Over time, these actions concentrate risk in ways that may seem manageable or even safe on the surface, but in reality, are pushing the financial system closer to a breaking point.

The release of this tension might come from a seemingly minor event—perhaps a small liquidity shock, an unexpected interest rate change, or a slight disruption in a key sector. Just as with tectonic pressure, the buildup of financial stress doesn’t need a large trigger to unleash major consequences. When stress reaches a tipping point, even a slight shock can result in rapid, cascading effects that rock the entire system, much like a minor tremor triggering a seismic shift.

“Risk accumulates silently, like tectonic pressure. The trigger may be minor, but the effect can be catastrophic.” Recognizing these hidden pressures allows investors to prepare for inevitable shocks—not by timing them, but by acknowledging the forces building beneath the surface.

The Bridge Collapse Analogy: Structural Weakness in Market Foundations

Imagine a bridge that spans a bustling river. On the surface, it appears solid, supporting the constant flow of traffic without issue. Yet, over time, small, undetected flaws start to develop within the bridge’s structure—corrosion in the metal, cracks in the concrete, or stress fractures in the supports. These seemingly minor issues go unnoticed for years, as the bridge continues to function as expected. But slowly, these weaknesses compound, compromising the overall integrity of the structure. One day, perhaps under the added weight of heavy traffic or an unexpected vibration, the bridge collapses, revealing the hidden decay that had been weakening it all along.

In financial markets, this quiet structural decay is all too familiar. Financial systems often depend heavily on low interest rates, accessible credit, or high leverage to fuel growth. As long as these conditions hold, the market appears robust and stable. However, this reliance on certain factors, like persistently low rates or expanding credit, creates unseen vulnerabilities. Over time, this dependency can erode the stability of the market’s foundations, just as hidden cracks weaken a bridge. Investors may continue to feel secure, unaware of the risk accumulating beneath the surface. But when a change occurs—like a rate hike or a sudden liquidity squeeze—the impact can be destabilizing, exposing the market’s underlying fragility.

This buildup of hidden vulnerabilities is something financial models often overlook. Traditional models and metrics tend to measure recent performance and assume that the foundations of the market are stable as long as they appear so. However, they fail to detect the “corrosion” of hidden leverage or the “cracks” of rate dependency. Just as engineers rely on maintenance checks to prevent bridge collapse, investors must continuously assess these structural risks rather than relying solely on surface-level stability.

“Financial models often overlook structural weaknesses, yet these unnoticed flaws can ultimately lead to collapse.” The strength of a market, like a bridge, depends on the resilience of its foundations. Recognizing and addressing these vulnerabilities is essential to withstand inevitable shifts.

The Floodplain Analogy: Overconfidence and the Risk of Flood

Consider a floodplain, a low-lying area that occasionally experiences flooding. After years of no major flood, locals start to assume the land is safe, and development begins. Homes, businesses, and even infrastructure are built closer and closer to the river’s edge. People grow confident, assured by the prolonged calm that the floodplain is no longer a high-risk area. Yet, the floodplain hasn’t changed—its exposure to flooding is as real as ever. When the waters eventually rise, the damage is far greater than it would have been if people had respected the inherent risk of the area.

In financial markets, investor behaviour often mirrors this floodplain mindset. Long stretches of calm in the market encourage a similar sense of security. Investors may start to believe that the absence of volatility means the market is inherently safe, tempting them to stretch their risk exposure. They increase leverage, concentrate their holdings, and seek higher returns, assuming that stability is the new norm. Like building on a floodplain, this behaviour gradually shifts investors into riskier positions, further from the protective “higher ground.”

This overconfidence is a natural reaction to prolonged stability, but it leaves investors vulnerable to the eventual “flood.” When a market shock inevitably arrives—whether through a sudden rate hike, economic downturn, or unexpected geopolitical event—the risk exposure built up over time reveals itself. Those investors who assumed safety based on recent calm are hit hardest, as their overconfidence led them to expand into riskier territories without adequate preparation.

Without visible risk, investors drift toward overconfidence, exposing themselves to the inevitable market ‘flood’.” Just as building on a floodplain increases risk, overestimating stability in financial markets can lead to significant exposure when volatility returns.

Bridging Theory with Practice: Preparing for the Unseen

The analogies used in this discussion on risk share a core theme: the gradual, often imperceptible accumulation of hidden vulnerabilities that eventually reach a tipping point, resulting in sudden and catastrophic outcomes. In each analogy—whether it’s an avalanche, earthquake, bridge collapse, or floodplain—small, seemingly harmless factors build up quietly over time, ultimately leading to a release of energy that defies prediction and overwhelms existing structures.

This buildup isn’t merely a static accumulation of risk; it reflects the behaviour of complex adaptive systems, where interdependencies and feedback loops can create cascading failures. In such systems, initial disturbances amplify as they interact with interconnected elements, much like a small spark in a dry forest can spread into a massive wildfire. When a forest hasn’t experienced a fire in a long time, dry twigs, leaves, and other fuel quietly accumulate, creating the conditions for an amplified, uncontrollable blaze. Similarly, in financial markets, periods of stability mask the silent buildup of interrelated risks, where leverage, liquidity dependencies, and concentrated exposures become increasingly interconnected, setting the stage for a cascading failure when the system is finally stressed.

In the face of these hidden, unpredictable pressures, traditional methods of predicting market behaviour fall short. David Dredge, in the excellent interview at the end of this post, introduces the concept of convexity as a more robust approach to managing such uncertainties. Rather than attempting to predict every market move, convexity focuses on building “good brakes” into a portfolio, emphasizing preparation over prediction. In a complex system, where seemingly minor shocks can amplify through interdependent relationships, a convex approach seeks to reduce vulnerability by designing a portfolio that responds adaptively, rather than rigidly, to market movements.

With convex investments, the goal is to create a system that can dynamically adapt to both upward momentum and sudden downturns, allowing for smoother navigation through the market’s unpredictable terrain. This approach is akin to preventive wildfire management, where controlled burns or strategic fuel reduction is implemented to reduce the severity of potential fires. By focusing on convexity, investors build resilience against unpredictable shocks, acknowledging that even prolonged stability can lead to hidden fragilities that, when released, cascade through the system with amplified force.

Portfolio Resilience: In practice, preparing for the unseen means building resilience into a portfolio by prioritizing structural strength over speculative forecasts. Here are a few key strategies for applying convexity:

  1. Diversification: Spread investments across a variety of asset classes and regions to reduce exposure to single-market shocks. True diversification goes beyond simple asset allocation, reaching into assets that are less correlated to traditional markets.
  2. Stress-Testing for Outliers: Test portfolio resilience against extreme, unlikely scenarios—those events that exist beyond the limits of historical backtesting. By running models that consider severe outlier events, investors can identify and prepare for vulnerabilities that standard risk models might overlook.
  3. Incorporating Convexity: Focus on adding convex assets or strategies that thrive under market stress. These could include trend following approaches, options, certain types of hedges, or alternative assets that offer asymmetric payoffs. Convex investments are designed to provide disproportionate returns during market upswings and increased protection during downturns, balancing risk with the potential for reward.

This approach reflects a fundamental shift: instead of relying on predictions or assuming calm periods will continue, investors are better served by building portfolios that can withstand both predictable and unpredictable shocks. Just as effective brakes allow a driver to safely navigate curves at high speeds, a convex portfolio allows investors to take advantage of market gains while limiting the damage from sudden losses.

“True resilience isn’t about predicting the future but building structures that withstand shocks.” In a world where risks often accumulate unseen, investing in a portfolio built on convexity and resilience is the most reliable way to stay prepared for what lies ahead. Embracing this mindset helps investors move beyond forecasting and toward a strategy that endures, regardless of the market’s next move.

Shifting from Prediction to Preparation

Throughout our exploration, we’ve seen that risk isn’t always visible or predictable; it often accumulates quietly, growing stronger beneath the surface. From the snowpack on a mountain to the fault lines in the earth, these hidden pressures build until a single event releases them with devastating force. Financial markets mirror these natural processes, where small, unnoticed risks can compound over time, creating a foundation for major disruptions. Traditional risk models, with their reliance on past data, fail to capture this gradual buildup, leaving investors exposed when unseen risks finally emerge.

The takeaway is clear: instead of relying on prediction, our focus should shift toward preparation. Questioning traditional backtesting methods and adopting a mindset that accounts for unseen risks can help investors build portfolios that withstand the unexpected. By embracing resilience, diversification, stress-testing, and convexity, investors can reduce their vulnerability to shocks and enhance their ability to weather any storm.

In a world where risk builds in silence, the best protection isn’t prediction—it’s preparation. Embracing this proactive mindset enables investors to move beyond the limits of prediction, fostering portfolios built to endure the inevitable surprises that lie ahead. Dave Dredge’s insights bring these principles into focus, illustrating how convexity in a portfolio acts as “good brakes,” allowing us to react to unforeseen market shocks without sacrificing long-term growth.

To delve deeper into the power of preparedness and the role of convexity in achieving resilient wealth compounding, listen to Dave’s full interview. His perspective sheds light on why, in complex financial systems, readiness is the key to enduring and thriving amidst the market’s inevitable surprises.

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