Efficient High-Dimensional Importance Sampling in Mixture Frameworks
42 Pages Posted: 26 Nov 2011
Date Written: June 8, 2011
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
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