MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES

Abstract

We examine the performances of several popular Levy jump models and some of the most sophisticated affine jump-diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Levy jump models using stock and option prices. We show that models with infinite-activity Levy jumps in returns significantly outperform affine jump-diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk-neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.