A Stochastic Volatility Model with Realized Measures for Option Pricing

Abstract

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence - the crucial parameter in pricing Standard and Poor's 500 Index options.

Keywords: Bayesian Inference, Monte Carlo Markov Chain, High-frequency, Realized volatility, HARG, Stochastic volatility, Option pricing

JEL Classification: C1, C11, C13, C15, C32, C58, G12, G13, G15