Matrix-State Particle Filter for Wishart Stochastic Volatility Processes

Roberto Casarin and Domenico Sartore

Working Papers from University of Brescia, Department of Economics

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.

Date: 2008
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (14)

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Working Paper: Matrix-State Particle Filter for Wishart Stochastic Volatility Processes (2007)
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