A Stochastic Simulation Approach to Model Selection for Stochastic Volatility Models

Stochastic volatility models have been widely appreciated in empirical finance such as option pricing, risk management, etc. Recent advances of Markov chain Monte Carlo (MCMC) techniques made it possible to fit all kinds of stochastic volatility models of increasing complexity within Bayesian framework. In this article, we propose a new Bayesian model selection procedure based on Bayes factor and a classical thermodynamic integration technique named path sampling to select an appropriate stochastic volatility model. The performance of the developed procedure is illustrated with an application to the daily pound/dollar exchange rates data set.