Deviance Information Criterion for Comparing Stochastic Volatility Models

Deviance Information Criterion for Comparing Stochastic Volatility Models

Bayesian methods have been efŽ cient in estimating parameters of stochastic volatility models for analyzing Ž nancial time series. Recent advances made it possible to Ž t 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 difŽ cult. The main ob...

Robust Deviance Information Criterion for Latent Variable Models∗

It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular a...

Comparing stochastic volatility models through Monte Carlo simulations

Stochastic volatility models are important tools for studying the behavior of many financial markets. For this reason a number of versions have been introduced and studied in the recent literature. The goal is to review and compare some of these alternatives by using Bayesian procedures. The quantity used to assess the goodness-of-fit is the Bayes factor, whereas the ability to forecast the vol...

Comparing Nested Models using Analysis of Deviance

Deviance Information Criteria for Missing Data Models

The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessment and model comparison is directly inspired by linear and generalised linear models, but it is open to different possible variations in the setting of missing data models, depending in particular on whether or not the missing variables are treated as parameters. In this paper, we reassess the c...