Efficient High-Dimensional Importance Sampling in Mixture Frameworks

42 Pages Posted: 26 Nov 2011

See all articles by Tore Selland Kleppe

Tore Selland Kleppe

University of Stavanger

Roman Liesenfeld

University of Cologne, Department of Economics

Date Written: June 8, 2011

Abstract

This paper provides high-dimensional and flexible importance sampling procedures for the likelihood evaluation of dynamic latent variable models involving finite or infinite mixtures leading to possibly heavy tailed and/or multi-modal target densities. Our approach is based upon the efficient importance sampling (EIS) approach of Richard and Zhang (2007) and exploits the mixture structure of the model when constructing importance sampling distributions as mixture of distributions. The proposed mixture EIS procedures are illustrated with ML estimation of a student-t state space model for realized volatilities and a stochastic volatility model with leverage effects and jumps for asset returns.

Keywords: Dynamic latent variable model, Importance sampling, Marginalized likelihood, Mixture, Monte Carlo, Realized Volatility, Stochastic volatility

JEL Classification: C15

Suggested Citation

Kleppe, Tore Selland and Liesenfeld, Roman, Efficient High-Dimensional Importance Sampling in Mixture Frameworks (June 8, 2011). Available at SSRN: https://ssrn.com/abstract=1964934 or http://dx.doi.org/10.2139/ssrn.1964934

Tore Selland Kleppe (Contact Author)

University of Stavanger ( email )

PB 8002
Stavanger, 4036
Norway

Roman Liesenfeld

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

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