Publication Type

Journal Article

Version

submittedVersion

Publication Date

2004

Abstract

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]

Keywords

Bayesian deviance; Jumps; Leverage effect; Markov chain Monte Carlo; Model com- plexity; Model selection.

Discipline

Applied Statistics | Econometrics

Research Areas

Econometrics

Publication

Journal of Business and Economic Statistics

Volume

22

Issue

1

First Page

107

Last Page

120

ISSN

0735-0015

Identifier

10.1198/073500103288619430

Publisher

American Statistical Association

Additional URL

https://doi.org/10.1198/073500103288619430

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