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Fast estimation of multivariate stochastic volatility

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  • Kostas Triantafyllopoulos
  • Giovanni Montana

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.

Suggested Citation

  • Kostas Triantafyllopoulos & Giovanni Montana, 2007. "Fast estimation of multivariate stochastic volatility," Papers 0708.4376, arXiv.org, revised Nov 2007.
    • Handle: RePEc:arx:papers:0708.4376
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    1. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-357, July.
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