Non-Normal Return Modeling

Non-normal return modeling involves using statistical distributions that account for the heavy tails and asymmetry observed in financial asset returns. Unlike the standard normal distribution, these models incorporate parameters for skewness and kurtosis to better reflect the realities of market crashes.

In the crypto domain, where price action is often characterized by sudden, violent moves, these models are superior for calculating Value at Risk and expected shortfall. By employing distributions like the Student t-distribution or stable distributions, analysts can better quantify the probability of extreme losses.

This approach is essential for institutions that need to survive in volatile environments. It shifts the focus from average outcomes to the reality of tail risks.