This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 174
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #307715
Title: Dynamic Correlation Structures in Factor Multivariate Stochastic Volatility Models
Author(s): Yu-Cheng Ku*+ and Peter Bloomfield
Companies: North Carolina State University and North Carolina State University
Address: , Raleigh, NC, 27695,
Keywords: Factor models ; Dynamic correlation ; Multivariate stochastic volatility ; Markov chain Monte Carlo ; Wishart process

We propose a dynamic factor multivariate stochastic volatility (MSV) model in which the evolution of the factor correlations/covariances is characterized by Wishart processes. There are two important features of this model. First, the factors are assumed to be latent and the covariance structure is fully unconstrained, which offers great model flexibility; secondly, both the individual volatilities and the intertemporal sensitivities are incorporated and hence we are more capable of capturing the time-varying factor correlations. From a dimension reduction point of view, the proposed model provides a unified framework for existing MSV models based on inverse-Wishart specifications. The estimation procedure is developed using Markov chain Monte Carlo (MCMC) methods. Finally, we fit the model with empirical data as a demonstration of real-world application.

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