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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

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  • Jean-Francois Richard
  • Roman Liesenfeld

Abstract

In this paper, Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as well as simulation smoothing where the latent volatilities are sampled at once. Based on this EIS simulation smoother a Bayesian Markov Chain Monte Carlo (MCDC) posterior analysis of the parameters of SV models can be performed.

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File URL: http://www.econ.pitt.edu/fantin/papers/class-anal_stochastic-volat.pdf
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Bibliographic Info

Paper provided by University of Pittsburgh, Department of Economics in its series Working Papers with number 322.

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Date of creation: Jun 2007
Date of revision: Jan 2004
Handle: RePEc:pit:wpaper:322

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  1. Shephard, N. & Pitt, M.K., 1995. "Likelihood Analysis of Non-Gaussian Parameter-Driven Models," Economics Papers 108, 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, American Statistical Association, vol. 20(1), pages 69-87, January.
  3. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers, Toulouse - GREMAQ 95.400, Toulouse - GREMAQ.
  4. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Eric Jacquier & Nicholas G. Polson & Peter Rossi, 1999. "Stochastic Volatility: Univariate and Multivariate Extensions," Computing in Economics and Finance 1999 112, Society for Computational Economics.
  6. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
  7. Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
  8. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, Econometric Society, vol. 57(6), pages 1317-39, November.
  9. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, Elsevier, vol. 10(4), pages 505-531, September.
  10. Neil Shephard & Siem Jan Koopman, 2002. "Testing the assumptions behind the use of importance sampling," Economics Series Working Papers 2002-W17, University of Oxford, Department of Economics.
  11. Lee Kai Ming & Koopman Siem Jan, 2004. "Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-17, May.
  12. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(3), pages 338-57, July.
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