DIC as a Model Comparison Criterion for Stochastic Volatility Models
by
Andreas Berg
University of Auckland
Coauthors: Renate Meyer, Jun Yu

Bayesian methods have been efficient in estimating parameters of Stochastic Volatility (SV) models for analysing financial time series. Recent advances made it possible to fit SV models of increasing complexity, including covariates, leverage effects, jump components and heavy-tailed distributions. However, a formal model comparison via Bayes factors remains difficult. The main objective of this paper is to demonstrate that model selection is better performed using the Deviance Information Criterion (DIC). It combines a Bayesian measure-of-fit with a measure of model complexity. We illustrate the performance of DIC in discriminating between various different SV models using simulated data and daily returns data on the S P 100 index.

Date received: May 26, 2002