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Autoregressive stochastic volatility models with heavy-tailed distributions: A comparison with multifactor volatility models

  • Asai, Manabu

This paper examines two asymmetric stochastic volatility models used to describe the heavy tails and volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student's t-distribution (ARSV-t), and the second is the multifactor stochastic volatility (MFSV) model. In order to estimate these models, the analysis employs the Monte Carlo likelihood (MCL) method proposed by Sandmann and Koopman [Sandmann, G., Koopman, S.J., 1998. Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics 87, 271-301.]. To guarantee the positive definiteness of the sampling distribution of the MCL, the nearest covariance matrix in the Frobenius norm is used. The empirical results using returns on the S&P 500 Composite and Tokyo stock price indexes and the Japan-US exchange rate indicate that the ARSV-t model provides a better fit than the MFSV model on the basis of Akaike information criterion (AIC) and the Bayes information criterion (BIC).

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Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 15 (2008)
Issue (Month): 2 (March)
Pages: 332-341

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Handle: RePEc:eee:empfin:v:15:y:2008:i:2:p:332-341
Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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  1. Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
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