Fast estimation of multivariate stochastic volatility

Kostas Triantafyllopoulos () and Giovanni Montana ()

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Abstract: In this paper we develop a Bayesian procedure for estimating multivariate stochastic volatility (MSV) using state space models. A multiplicative model based on inverted Wishart and multivariate singular beta distributions is proposed for the evolution of the volatility, and a flexible sequential volatility updating is employed. Being computationally fast, the resulting estimation procedure is particularly suitable for on-line forecasting. Three performance measures are discussed in the context of model selection: the log-likelihood criterion, the mean of standardized one-step forecast errors, and sequential Bayes factors. Finally, the proposed methods are applied to a data set comprising eight exchange rates vis-a-vis the US dollar.

Date: 2007-08, Revised 2007-11
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:0708.4376

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