Deviance Information Criterion for Comparing Stochastic Volatility Models

• Main Authors: Berg, Andreas, Meyer, Renate, YU, Jun
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2004
• 主題: Bayesian deviance; Jumps; Leverage effect; Markov chain Monte Carlo; Model com- plexity; Model selection.; Applied Statistics; Econometrics
• 在線閱讀: https://ink.library.smu.edu.sg/soe_research/351; https://ink.library.smu.edu.sg/context/soe_research/article/1350/viewcontent/SSRN_id320023__1_.pdf

Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility 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 article is to demonstrate that model selection is more easily 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 stochastic volatility models using simulated data and daily returns data on the Standard & Poors (S&P) 100 index. [PUBLICATION ABSTRACT]