Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
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- Roman Liesenfeld & Jean-Francois Richard, 2006. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, pages 335-360.
- Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, pages 2753-2762.
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- Skaug, Hans J. & Yu, Jun, 2014.
"A flexible and automated likelihood based framework for inference in stochastic volatility models,"
Computational Statistics & Data Analysis,
Elsevier, pages 642-654.
- Gian Piero Aielli & Massimiliano Caporin, 2011. "Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators," "Marco Fanno" Working Papers 0133, Dipartimento di Scienze Economiche "Marco Fanno".
- Kleppe, Tore Selland & Yu, Jun & Skaug, Hans J., 2014. "Maximum likelihood estimation of partially observed diffusion models," Journal of Econometrics, Elsevier, pages 73-80.
- Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, pages 2350-2364.
- Roman Liesenfeld & Jean-Francois Richard, 2006.
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- 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.
- Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.
- Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, pages 1385-1411.
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