Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
AbstractIn this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective. Further, we validate the two methods using diagnostic importance sampling test procedures. Stochastic volatility models with Gaussian and Student-t distributed disturbances are considered.
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Bibliographic InfoArticle provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.
Volume (Year): 8 (2004)
Issue (Month): 2 (May)
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- Jean-Francois Richard & Roman Liesenfeld, 2007.
"Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models,"
322, University of Pittsburgh, Department of Economics, revised Jan 2004.
- Roman Liesenfeld & Jean-Francois Richard, 2006. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 335-360.
- Liesenfeld, Roman & Richard, Jean-François, 2004. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Economics Working Papers 2004,12, Christian-Albrechts-University of Kiel, Department of Economics.
- Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
- Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
- Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.
- Jean-Francois Richard, 2007.
"Efficient High-Dimensional Importance Sampling,"
321, University of Pittsburgh, Department of Economics, revised Jan 2007.
- Hans J. Skaug & Jun Yu, 2007.
"Automated Likelihood Based Inference for Stochastic Volatility Models,"
CoFie-01-2007, Sim Kee Boon Institute for Financial Economics.
- Jun Yu, 2007. "Automated Likelihood Based Inference for Stochastic Volatility Models," Working Papers 01-2007, Sim Kee Boon Institute for Financial Economics.
- Hans J. Skaug & Jun Yu, 2009. "Automated Likelihood Based Inference for Stochastic Volatility Models," Working Papers 15-2009, Singapore Management University, School of Economics.
- Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2753-2762, November.
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