Dynamic factor, leverage and realized covariances in multivariate stochastic volatility

TL;DR

This paper introduces a dynamic factor model incorporating leverage effects and realized measures to improve stability and performance in multivariate stochastic volatility modeling of stock returns.

Contribution

It proposes a novel dynamic factor model that integrates realized covariances and leverage effects, addressing instability issues in high-dimensional stochastic volatility models.

Findings

Model shows stable portfolio performance improvements

Incorporates high-frequency data for better covariance estimation

Addresses parameter instability in high-dimensional settings

Abstract

In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the SP500 index and the model is shown to have a stable advantage in portfolio performance.