The Variance-Covariance method, also known as the parametric method, is a statistical approach used to calculate Value-at-Risk (VaR) by assuming that asset returns follow a normal distribution. This method relies on historical data to estimate the mean returns, standard deviations (volatilities), and correlations (covariances) of assets within a portfolio. It provides a straightforward calculation of potential losses based on these parameters.
Calculation
The calculation involves multiplying the portfolio’s standard deviation by a chosen confidence level multiplier (e.g., 1.65 for 95% confidence) to determine the maximum expected loss. The method’s simplicity makes it widely used for estimating risk in portfolios with linear exposures. However, its reliance on historical data and the assumption of normal distribution can lead to underestimation of risk during periods of high volatility or market stress, especially in crypto markets with heavy tails.
Assumption
A critical assumption of the Variance-Covariance method is that asset returns are normally distributed and correlations remain constant over time. In the context of cryptocurrency derivatives, this assumption often fails during extreme market events, where correlations tend to increase significantly. For this reason, more advanced methods like historical simulation or Monte Carlo simulation are often preferred for accurately quantifying tail risk in volatile digital asset markets.
Meaning ⎊ Portfolio VaR Calculation establishes the statistical maximum loss threshold for crypto derivatives, ensuring systemic solvency through correlation-aware risk modeling.
Meaning ⎊ Value at Risk calculation in crypto options quantifies potential portfolio losses under specific confidence levels, guiding margin requirements and assessing protocol solvency.
