Deviance Information Criterion for Comparing Stochastic Volatility Models
展示如何用偏差信息准则（DIC）来比较复杂的随机波动模型，避免贝叶斯因子计算困难，并用模拟数据和标普100指数日收益率数据验证其效果。
AbstractBayesian 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.KEY WORDS: Bayesian devianceJumpsLeverage effectMarkov chain Monte CarloModel complexityModel selection