Non-Gaussian Modeling

Non-Gaussian modeling involves using mathematical distributions that do not assume a normal bell curve, better capturing the reality of financial markets. Because asset returns often exhibit skewness and fat tails, standard Gaussian models often fail to predict extreme risks.

Non-Gaussian models, such as Levy stable distributions or jump-diffusion models, provide a more accurate framework for pricing options and managing risk. These models account for the fact that prices can jump instantaneously rather than moving in a continuous path.

By moving beyond Gaussian assumptions, quantitative researchers can create more realistic simulations of market behavior. This is fundamental for sophisticated risk management in the crypto derivatives space.