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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.16837 |
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Table des matières:
- 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.