Matrix-State Particle Filter for Wishart Stochastic Volatility Processes

16 Pages Posted: 25 Jan 2008

See all articles by Roberto Casarin

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Domenico Sartore

Ca Foscari University of Venice - Dipartimento di Economia

Abstract

This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We focus on a special class of models recently proposed in the literature and assume that the covariance matrix is a latent variable which follows an autoregressive Wishart process. We review two alternative stochastic representations of the Wishart process and propose Markov-Switching Wishart processes to capture different regimes in the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for matrix-valued distributions and allows us to sequentially estimate both the parameters and the latent variables.

Keywords: Multivariate Stochastic Volatility, Matrix-State Particle Filters, Sequential Monte Carlo, Wishart Processes, Markov Switching

JEL Classification: C11, C15, C32

Suggested Citation

Casarin, Roberto and Sartore, Domenico, Matrix-State Particle Filter for Wishart Stochastic Volatility Processes. University Ca' Foscari of Venice, Department of Economics Research Paper No. 30/WP/2007, Available at SSRN: https://ssrn.com/abstract=1087235

University of Venice Dept. of Econ. Submitter (Contact Author)

Ca Foscari University of Venice ( email )

Cannaregio 873
Venice, Venice 30121
Italy

HOME PAGE: http://www.dse.unive.it/pubblicazioni/working-papers/

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics ( email )

San Giobbe 873/b
Venice, 30121
Italy
+39 030.298.91.49 (Phone)
+39 030.298.88.37 (Fax)

HOME PAGE: http://sites.google.com/view/robertocasarin

Domenico Sartore

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

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