Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

TL;DR

This paper introduces two novel interweaving strategies to significantly improve Bayesian estimation efficiency for multivariate factor stochastic volatility models, enabling faster convergence and better performance in high-dimensional financial data analysis.

Contribution

The paper proposes easy-to-implement interweaving strategies that exploit non-identifiability in factor models to accelerate MCMC convergence with minimal additional computational cost.

Findings

Boosts MCMC efficiency by several orders of magnitude

Demonstrates superior performance on 26-dimensional exchange rate data

Extensive simulations confirm improved convergence and mixing

Abstract

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.