Value at Risk

Value at Risk (VaR) quantifies the maximum expected loss on a portfolio over a specific time horizon at a given confidence level, providing a single number that captures downside risk. A portfolio with 1-day 95% VaR of $10,000 means there's a 95% probability of losing less than $10,000 in a single day, equivalently, a 5% probability of losing more. The metric originated in traditional finance risk management, where banks and funds use it to set capital reserves, measure trader risk limits, and report to regulators. Calculating VaR requires modeling the distribution of portfolio returns. Historical simulation uses past return data; parametric methods assume normal distributions; Monte Carlo simulation generates thousands of scenarios. Each approach has different strengths for different portfolio types. VaR has significant limitations that the 2008 financial crisis exposed. It doesn't describe what happens in the worst 5% of cases, the tail risk beyond the confidence level could be catastrophic. It assumes historical patterns predict future risk, which fails during regime changes. It can be gamed by structuring positions to look safe under VaR while carrying hidden tail risk. Conditional VaR (CVaR or Expected Shortfall) addresses some limitations by measuring expected loss given that you're in the worst percentile. In crypto's volatile markets, VaR calculations require careful handling of fat-tailed distributions and regime-dependent volatility.