The Fallacy of Value-at-Risk (VaR)
The Problem with “Normal” Distributions
Over-Simplified Correlations
The Risk Tolerance Trap
The Solution?
Predict crashes before they crush your portfolio
1. The Fallacy of Value-at-Risk (VaR)
Many advisors rely on VaR to measure risk, but it has a critical flaw: it only predicts losses under normal market conditions. VaR tells you, “99% of the time, you won’t lose more than X.” But what about the 1% when markets collapse? VaR fails to capture extreme events like 2008 or COVID-19 where losses spiral beyond predictions. Worse, VaR recalculates after volatility spikes, leaving clients unprepared.
2. The Problem with “Normal” Distributions
Most risk models assume returns follow a bell curve, but real-world crises defy this. During stress, correlations shift, diversification breaks, and losses compound unpredictably. Relying on historical norms can dangerously underestimate tail risks.
3. Over-Simplified Correlations
Analyzing assets purely through correlation matrices is unreliable. Stocks, bonds, and commodities interact differently under stress. A model that works in calm markets may crumble when geopolitics or liquidity shocks hit.
4. The Risk Tolerance Trap
Questionnaires measuring client risk tolerance are flawed human psychology changes with market cycles. A client who’s “aggressive” in a bull market may panic-sell in a crash. Risk capacity (quantitative ability to absorb losses) is far more critical.
The Solution? Forward-Looking Stress Testing
Unlike VaR, stress testing examines how portfolios behave in crises before they happen. It’s the only way to prepare clients for the unpredictable.
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