On the observed-data deviance information criterion for volatility modeling

Chan, JCC; Grant, AL

On the observed-data deviance information criterion for volatility modeling

Chan, JCC

Grant, AL

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Publication Type:: Journal Article
Citation:: Journal of Financial Econometrics, 2016, 14 (4), pp. 772 - 802
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Grant, AL https://orcid.org/0000-0002-6167-6130	en_US
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	Journal of Financial Econometrics, 2016, 14 (4), pp. 772 - 802	en_US
dc.identifier.issn	1479-8409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122925
dc.description.abstract	© The Author, 2016. Published by Oxford University Press. All rights reserved. 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.	en_US
dc.relation.ispartof	Journal of Financial Econometrics	en_US
dc.relation.isbasedon	10.1093/jjfinec/nbw002	en_US
dc.subject.classification	Econometrics	en_US
dc.title	On the observed-data deviance information criterion for volatility modeling	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	14	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© The Author, 2016. Published by Oxford University Press. All rights reserved. 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.

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http://hdl.handle.net/10453/122925