
Every now and then the market reminds you that the most dangerous risks aren’t the ones you can see coming. They’re the ones embedded in the risk management systems themselves. Value-at-Risk (VaR) shocks are one of those paradoxes in finance where the tools designed to keep portfolios safe can end up making everything worse. Understanding how they work, and why they matter, is essential for anyone managing leveraged portfolios or trying to make sense of the violent dislocations that periodically rip through markets.
The basic idea is straightforward enough. Many portfolios size their positions based on some measure of expected risk – typically volatility, asset correlation, and the resulting VaR. In calm markets, volatility is low, correlations are well-behaved, and risk models give you the green light to run bigger positions. Life is good, until something upsets the apple cart. Volatility spikes, correlations jump, everybody’s risk model says the same thing at the same time – cut risk.
Below is a deliberately simplified illustrative simulation to walk through the mechanics of a VaR shock. Think of it as a stylized version of what happens in the real world when a stress event hits a volatility-targeted portfolio, whether it’s a risk parity fund, a sector and factor neutral fund, a spread trader or a trend follower. Any leveraged strategy that dynamically adjusts exposure based on realized risk metrics.
We use two assets, each running around 15% annualized volatility in normal times, with correlations near zero. The portfolio targets a 15% volatility max and sizes positions accordingly. With two uncorrelated assets at 15% vol each, the math is generous as the diversification benefit from near-zero correlation means you can hold around 70-80% in each asset (roughly 1.5x total exposure) and still run portfolio-level volatility comfortably below the 15% limit, closer to 10%. That’s the beauty of diversification working as intended. Low correlations give you leverage capacity for free. The risk model sees calm vol, calm correlations, and allocates accordingly. Everything is in equilibrium. Now let’s break it.
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