Parametric VaR

Parametric VaR, also known as the variance-covariance method, uses the assumption that returns follow a normal distribution to calculate Value at Risk. It relies on the mean and standard deviation of asset returns to determine the potential loss at a given confidence level.

While this method is computationally efficient and easy to implement, it is often inaccurate for cryptocurrency and derivative markets because these assets rarely follow a normal distribution. They often exhibit fat tails and skewness, which the parametric approach fails to capture.

Consequently, it may significantly underestimate the risk of extreme losses. It is best used for portfolios with assets that exhibit stable, predictable behavior.

In highly volatile markets, it should be used with caution and supplemented by other methods. It provides a quick snapshot of risk based on statistical parameters.

It is a fundamental approach for understanding portfolio risk under standard assumptions. It serves as a baseline for more complex risk models.