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Comparison of MCMC Methods for Estimating Stochastic Volatility Models

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  • Manabu Asai

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Abstract

This article investigates performances of MCMC methods to estimate stochastic volatility models on simulated and real data. There are two efficient MCMC methods to generate latent volatilities from their full conditional distribution. One is the mixture sampler and the other is the multi-move sampler. There is another efficient method for latent volatilities and all parameters called the integration sampler, which is based on the mixture sampler. This article proposes an alternative method based on the multi-move sampler and finds evidence that it is the best method among them. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-2974-4
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 25 (2005)
Issue (Month): 3 (June)
Pages: 281-301

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Handle: RePEc:kap:compec:v:25:y:2005:i:3:p:281-301

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: integration sampler; Markov chain Monte Carlo; mixture sampler; multi-move sampler; simulation smoother;

References

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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.
  2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  3. A. W. Coats, 1996. "Introduction," History of Political Economy, Duke University Press, vol. 28(5), pages 3-11, Supplemen.
  4. 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.
  5. Renate Meyer & David A. Fournier & Andreas Berg, 2003. "Stochastic volatility: Bayesian computation using automatic differentiation and the extended Kalman filter," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 408-420, December.
  6. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
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Cited by:
  1. Manabu Asai & Michael McAleer, 2010. "Alternative Asymmetric Stochastic Volatility Models," Working Papers in Economics 10/70, University of Canterbury, Department of Economics and Finance.
  2. Asai, Manabu & McAleer, Michael, 2009. "The structure of dynamic correlations in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 150(2), pages 182-192, June.
  3. Asai, Manabu, 2009. "Bayesian analysis of stochastic volatility models with mixture-of-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2579-2596.

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