2007/08/31 by Kostas Triantafyllopoulos, Triantafyllopoulos, Kostas, Giovanni Montana +1
Economics, Econometrics and Finance · #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.0708.4376
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.