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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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References

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1. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
   
   • Gallant, A. Ronald & Hsu, Chien-Te & Tauchen, George, 2000. "Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance," Working Papers 00-04, Duke University, Department of Economics.
2. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
   
   • Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-30, August.
3. 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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9. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-17, October.
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11. Danielsson, Jon, 1994. "Stochastic volatility in asset prices estimation with simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 375-400.
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13. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
14. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
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Cited by:

1. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," KIER Working Papers 758, Kyoto University, Institute of Economic Research.
   
   • Asai, Manabu & McAleer, Michael & Medeiros, Marcelo C., 2012. "Modelling and forecasting noisy realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 217-230, January.
   • Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," Documentos del Instituto Complutense de Análisis Económico 2011-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
   • Asai, M. & McAleer, M.J. & Medeiros, M., 2011. "Modelling and Forecasting Noisy Realized Volatility," Econometric Institute Report EI 2011-05, Erasmus University Rotterdam, Econometric Institute.
   • 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.
   • Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2009. "Modelling and Forecasting Noisy Realized Volatility," CIRJE F-Series CIRJE-F-669, CIRJE, Faculty of Economics, University of Tokyo.
2. Manabu Asai & Michael McAleer, 2005. "Asymmetric Multivariate Stochastic Volatility," DEA Working Papers 12, Universitat de les Illes Balears, Departament d'Economía Aplicada.
   
   • Manabu Asai & Michael McAleer, 2006. "Asymmetric Multivariate Stochastic Volatility," Econometric Reviews, Taylor and Francis Journals, vol. 25(2-3), pages 453-473.
3. 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.
4. Manabu Asai & Massimiliano Caporin & Michael McAleer, 2012. "Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models," Documentos del Instituto Complutense de Análisis Económico 2012-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
   
   • Michael McAleer & Manabu Asai & Massimiliano Caporin, 2012. "Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models," KIER Working Papers 812, Kyoto University, Institute of Economic Research.
   • Manabu Asai & Massimiliano Caporin & Michael McAleer, 2012. "Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models," Working Papers in Economics 12/04, University of Canterbury, Department of Economics and Finance.
   • Asai, M. & Caporin, M. & McAleer, M.J., 2012. "Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models," Econometric Institute Report EI 2012-02, Erasmus University Rotterdam, Econometric Institute.

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