Matrix-State Particle Filter for Wishart Stochastic Volatility Processes
Roberto Casarin () and
Domenico Sartore ()
No 2007_30, Working Papers from Department of Economics, University of Venice "Ca' Foscari"
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. (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 (search for similar items in EconPapers)
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Working Paper: Matrix-State Particle Filter for Wishart Stochastic Volatility Processes (2008)
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