Distribution Fitting Tests

Algorithm

Distribution fitting tests, within cryptocurrency and derivatives, assess how well theoretical distributions model observed price data or portfolio returns. These tests are crucial for validating assumptions underlying pricing models, risk management frameworks, and trading strategies, particularly for exotic options or structured products where analytical solutions are limited. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared tests are frequently employed to determine if sample data deviates significantly from a hypothesized distribution, such as log-normal or Student’s t, informing parameter calibration and model selection. Accurate distribution fitting directly impacts the reliability of Value-at-Risk calculations and expected shortfall estimations, essential for regulatory compliance and capital allocation.