On the observed-data deviance information criterion for volatility modeling

Joshua C.C. Chan*, Angelia L. Grant

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    64 Citations (Scopus)

    Abstract

    We propose importance sampling algorithms based on fast band matrix routines for estimating the observed-data likelihoods for a variety of stochastic volatility models. This is motivated by the problem of computing the deviance information criterion (DIC)-a popular Bayesian model comparison criterion that comes in a few variants. Although the DIC based on the conditional likelihood-obtained by conditioning on the latent variables-is widely used for comparing stochastic volatility models, recent studies have argued against its use on both theoretical and practical grounds. Indeed, we show via a Monte-Carlo study that the conditional DIC tends to favor overfitted models, whereas the DIC based on the observed-data likelihood-calculated using the proposed importance sampling algorithms-seems to perform well. We demonstrate the methodology with an application involving daily returns on the Standard & Poors 500 index.

    Original languageEnglish
    Pages (from-to)772-802
    Number of pages31
    JournalJournal of Financial Econometrics
    Volume14
    Issue number4
    DOIs
    Publication statusPublished - 1 Sept 2016

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