Modeling and Simulation of Financial Returns under Non-Gaussian Distributions

TL;DR

This paper compares various non-Gaussian models for high-frequency financial returns, analyzing their statistical properties and implications for risk management and option pricing.

Contribution

It provides a comprehensive comparison of popular non-Gaussian return models and explores their effects on option pricing beyond the Black-Scholes framework.

Findings

Models show consistency in capturing large price change scaling.

Non-Gaussian models better reproduce empirical tail behavior.

Impacts on option pricing are significant compared to Gaussian assumptions.

Abstract

It is well known that the probability distribution of high-frequency financial returns is characterized by a leptokurtic, heavy-tailed shape. This behavior undermines the typical assumption of Gaussian log-returns behind the standard approach to risk management and option pricing. Yet, there is no consensus on what class of probability distributions should be adopted to describe financial returns and different models used in the literature have demonstrated, to varying extent, an ability to reproduce empirically observed stylized facts. In order to provide some clarity, in this paper we perform a thorough study of the most popular models of return distributions as obtained in the empirical analyses of high-frequency financial data. We compare the statistical properties and simulate the dynamics of non-Gaussian financial fluctuations by means of Monte Carlo sampling from the different models in terms of realistic tail exponents. Our findings show a noticeable consistency between the considered return distributions in the modeling of the scaling properties of large price changes. We also discuss the convergence rate to the asymptotic distributions of the non-Gaussian stochastic processes and we study, as a first example of possible applications, the impact of our results on option pricing in comparison with the standard Black and Scholes approach.