Box-Cox Stochastic Volatility Models with Heavy-Tails and Correlated Errors

Xibin Zhang () and Maxwell King

No 26/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper presents a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent stochastic processes in the asymmetric stochastic volatility (SV) model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution and the marginal density of asset returns has heavytails. To test for the significance of the Box-Cox transformation parameter, we present the likelihood ratio statistic, in which likelihood functions can be approximated using a particle filter and a Monte Carlo kernel likelihood. When applying the heavy-tailed asymmetric Box-Cox SV model and the proposed sampling algorithm to continuously compounded daily returns of the Australian stock index, we find significant empirical evidence supporting the Box-Cox transformation of the squared volatility against the alternative model involving a logarithmic transformation.

Keywords: Leverage effect; Likelihood ratio test; Markov Chain Monte Carlo; Monte Carlo kernel likelihood; Particle filter (search for similar items in EconPapers)
JEL-codes: C12 C15 C52 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2004-11
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-fin
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Box-Cox stochastic volatility models with heavy-tails and correlated errors (2008)
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