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

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  • Asai, Manabu

Abstract

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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Bibliographic Info

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

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Web page: http://www.elsevier.com/locate/jempfin

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Citations

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Cited by:
  1. Manabu Asai & Massimiliano Caporin & Michael McAleer, 2013. "Forecasting Value-at-Risk using Block Structure Multivariate Stochastic Volatility Models," Tinbergen Institute Discussion Papers 13-073/III, Tinbergen Institute.
  2. Manabu Asai & Massimiliano Caporin & Michael McAleer, 2012. "Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models," Documentos de Trabajo del ICAE 2012-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  3. Manabu Asai & Michael McAleer, 2010. "Alternative Asymmetric Stochastic Volatility Models," Working Papers in Economics 10/70, University of Canterbury, Department of Economics and Finance.
  4. Manuabu Asai & Michael McAleer & Marcelo C. Medeiros, 2010. "Modelling and Forecasting Noisy Realized Volatility," Working Papers in Economics 10/21, University of Canterbury, Department of Economics and Finance.
  5. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.
  6. Ahmed Hachicha & Fatma Hachicha & Afif Masmoudi, 2013. "SV Mixture, Classification Using EM Algorithm," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(4), pages 553-559, April.
  7. Manabu Asai & Michael McAleer, 2006. "Asymmetric Multivariate Stochastic Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 453-473.
  8. Wang, Joanna J.J. & Chan, Jennifer S.K. & Choy, S.T. Boris, 2011. "Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 852-862, January.

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