Bayesian analysis of constant elasticity of variance models

Chan, JSK; Choy, STB; Lee, ABW

Bayesian analysis of constant elasticity of variance models

Chan, JSK Choy, STB Lee, ABW

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Publication Type:: Journal Article
Citation:: Applied Stochastic Models in Business and Industry, 2007, 23 (1), pp. 83 - 96
Issue Date:: 2007-01-01

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Field	Value	Language
dc.contributor.author	Chan, JSK	en_US
dc.contributor.author	Choy, STB	en_US
dc.contributor.author	Lee, ABW	en_US
dc.date.issued	2007-01-01	en_US
dc.identifier.citation	Applied Stochastic Models in Business and Industry, 2007, 23 (1), pp. 83 - 96	en_US
dc.identifier.issn	1524-1904	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3402
dc.description.abstract	This paper focuses on the estimation of some models in finance and in particular, in interest rates. We analyse discretized versions of the constant elasticity of variance (CEV) models where the normal law showing up in the usual discretization of the diffusion part is replaced by a range of heavy-tailed distributions. A further extension of the model is to allow the elasticity of variance to be a parameter itself. This generalized model allows great flexibility in modelling and simplifies the model implementation considerably using the scale mixtures representation. The mixing parameters provide a means to identify possible outliers and protect inference by down-weighting the distorting effects of these outliers. For parameter estimation, Bayesian approach is adopted and implemented using the software WinBUGS (Bayesian inference using Gibbs sampler). Results from a real data analysis show that an exponential power distribution with a random shape parameter, which is highly leptokurtic compared with the normal distribution, forms the best CEV model for the data. Copyright © 2006 John Wiley & Sons, Ltd.	en_US
dc.relation.ispartof	Applied Stochastic Models in Business and Industry	en_US
dc.relation.isbasedon	10.1002/asmb.639	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Bayesian analysis of constant elasticity of variance models	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	23	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0102 Applied Mathematics	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 Science
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

This paper focuses on the estimation of some models in finance and in particular, in interest rates. We analyse discretized versions of the constant elasticity of variance (CEV) models where the normal law showing up in the usual discretization of the diffusion part is replaced by a range of heavy-tailed distributions. A further extension of the model is to allow the elasticity of variance to be a parameter itself. This generalized model allows great flexibility in modelling and simplifies the model implementation considerably using the scale mixtures representation. The mixing parameters provide a means to identify possible outliers and protect inference by down-weighting the distorting effects of these outliers. For parameter estimation, Bayesian approach is adopted and implemented using the software WinBUGS (Bayesian inference using Gibbs sampler). Results from a real data analysis show that an exponential power distribution with a random shape parameter, which is highly leptokurtic compared with the normal distribution, forms the best CEV model for the data. Copyright © 2006 John Wiley & Sons, Ltd.

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http://hdl.handle.net/10453/3402