Spillovers and Co-movements in Multivariate Volatility: A Vector Multiplicative Error Model

TL;DR

This paper introduces a new multivariate volatility model within the MEM framework that captures spillover and co-movement effects, offering computational efficiency and improved performance in analyzing financial asset volatilities.

Contribution

The paper develops a novel vector MEM model with a latent component for spillovers and co-movement, incorporating a clustering procedure for parameter reduction, and demonstrates its effectiveness on Dow Jones assets.

Findings

Model effectively captures spillover and co-movement effects.

Proposed approach outperforms or matches alternative models.

Clustering reduces complexity without sacrificing accuracy.

Abstract

Recent developments in financial time series focus on modeling volatility across multiple assets or indices in a multivariate framework, accounting for potential interactions such as spillover effects. Furthermore, the increasing integration of global financial markets provides a similar dynamics (referred to as comovement). In this context, we introduce a novel model for volatility vectors within the Multiplicative Error Model (MEM) class. This framework accommodates both spillover and co-movement effects through a distinct latent component. By adopting a specific parameterization, the model remains computationally feasible even for high-dimensional volatility vectors. To reduce the number of unknown coefficients, we propose a simple model-based clustering procedure. We illustrate the effectiveness of the proposed approach through an empirical application to 29 assets of the Dow Jones Industrial Average index, providing insight into volatility spillovers and shared market dynamics. Comparative analysis against alternative vector MEMs, including a fully parameterized version of the proposed model, demonstrates its superior or at least comparable performance across multiple evaluation criteria.