Modelling stochastic volatility using generalized t distribution

Wang, JJJ; Choy, STB; Chan, JSK

Modelling stochastic volatility using generalized t distribution

Wang, JJJ

Choy, STB

Chan, JSK

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Publication Type:: Journal Article
Citation:: Journal of Statistical Computation and Simulation, 2013, 83 (2), pp. 340 - 354
Issue Date:: 2013-02-01

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Field	Value	Language
dc.contributor.author	Wang, JJJ https://orcid.org/0000-0003-0088-7003	en_US
dc.contributor.author	Choy, STB https://orcid.org/0000-0002-6861-6974	en_US
dc.contributor.author	Chan, JSK	en_US
dc.date.issued	2013-02-01	en_US
dc.identifier.citation	Journal of Statistical Computation and Simulation, 2013, 83 (2), pp. 340 - 354	en_US
dc.identifier.issn	0094-9655	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117171
dc.description.abstract	In modelling financial return time series and time-varying volatility, the Gaussian and the Student-t distributions are widely used in stochastic volatility (SV) models. However, other distributions such as the Laplace distribution and generalized error distribution (GED) are also common in SV modelling. Therefore, this paper proposes the use of the generalized t (GT) distribution whose special cases are the Gaussian distribution, Student-t distribution, Laplace distribution and GED. Since the GT distribution is a member of the scale mixture of uniform (SMU) family of distribution, we handle the GT distribution via its SMU representation. We show this SMU form can substantially simplify the Gibbs sampler for Bayesian simulation-based computation and can provide a mean of identifying outliers. In an empirical study, we adopt a GT-SV model to fit the daily return of the exchange rate of Australian dollar to three other currencies and use the exchange rate to US dollar as a covariate. Model implementation relies on Bayesian Markov chain Monte Carlo algorithms using the WinBUGS package. © 2013 Copyright Taylor and Francis Group, LLC.	en_US
dc.relation.ispartof	Journal of Statistical Computation and Simulation	en_US
dc.relation.isbasedon	10.1080/00949655.2011.608067	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Modelling stochastic volatility using generalized t distribution	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	83	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	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 Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	83	en_US

Abstract:

In modelling financial return time series and time-varying volatility, the Gaussian and the Student-t distributions are widely used in stochastic volatility (SV) models. However, other distributions such as the Laplace distribution and generalized error distribution (GED) are also common in SV modelling. Therefore, this paper proposes the use of the generalized t (GT) distribution whose special cases are the Gaussian distribution, Student-t distribution, Laplace distribution and GED. Since the GT distribution is a member of the scale mixture of uniform (SMU) family of distribution, we handle the GT distribution via its SMU representation. We show this SMU form can substantially simplify the Gibbs sampler for Bayesian simulation-based computation and can provide a mean of identifying outliers. In an empirical study, we adopt a GT-SV model to fit the daily return of the exchange rate of Australian dollar to three other currencies and use the exchange rate to US dollar as a covariate. Model implementation relies on Bayesian Markov chain Monte Carlo algorithms using the WinBUGS package. © 2013 Copyright Taylor and Francis Group, LLC.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/117171