Parametric VAR Limitations

Parametric VAR limitations stem primarily from the assumption that asset returns follow a normal, or Gaussian, distribution. This model uses the mean and standard deviation of returns to estimate risk, which fails to account for the fat tails, or leptokurtosis, commonly observed in financial markets.

In the crypto domain, extreme events occur far more frequently than a normal distribution predicts, rendering parametric VAR dangerously inadequate during market crashes. The model ignores non-linear risks, such as those embedded in options and complex derivatives, which cannot be captured by simple standard deviation.

Furthermore, it assumes that correlations between assets remain stable, which often breaks down exactly when diversification is needed most. This failure to capture skewness and kurtosis leads to a systematic underestimation of tail risk.

Relying solely on parametric methods can provide a false sense of security, encouraging excessive leverage. It is a simplified approach that misses the structural complexities of order flow and liquidity shocks.

Sophisticated risk managers prefer simulation-based methods to address these inherent flaws.