🤖 AI Summary
This study addresses the challenge of jointly modeling volatility spillovers and co-movements in multi-asset settings by proposing a novel Vector Multiplicative Error Model (Vector MEM). The model incorporates a latent variable structure to simultaneously capture cross-asset volatility spillovers and co-movement dynamics, while introducing a model-driven clustering strategy to reduce parameter dimensionality and enhance scalability in high-dimensional contexts. This approach represents the first integration of both mechanisms within the MEM framework, balancing expressive power with computational tractability. Empirical analysis based on 29 Dow Jones constituents demonstrates that the proposed model outperforms or matches existing Vector MEM methods across multiple evaluation metrics, effectively uncovering the transmission pathways and co-movement structure of market volatility.
📝 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.