Factor multivariate stochastic volatility models of high dimension

TL;DR

This paper introduces a factor model-based multivariate stochastic volatility framework that addresses high-dimensionality issues, with a two-stage estimation process and demonstrated effectiveness through simulations and portfolio applications.

Contribution

It develops a novel two-stage estimation procedure for high-dimensional multivariate stochastic volatility models using factor decomposition.

Findings

Estimation procedures have desirable asymptotic properties.

Simulation experiments show accurate volatility estimation.

Application to portfolio allocation demonstrates practical usefulness.

Abstract

Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework. We propose a two-stage estimation procedure for the fMSV model: in the first stage, estimators of the factor model are obtained, and in the second stage, the MSV component is estimated using the estimated common factor variables. We derive the asymptotic properties of the estimators, taking into account the estimation of the factor variables. The prediction performances are illustrated by finite-sample simulation experiments and applications to portfolio allocation.