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.

翻译：金融时间序列的最新发展聚焦于在多元框架下对多个资产或指数的波动率进行建模，以考虑潜在的交互作用，如溢出效应。此外，全球金融市场日益增强的整合性带来了类似的动态特征（称为联动性）。在此背景下，我们为乘法误差模型（MEM）类引入了一种新颖的波动率向量模型。该框架通过一个独特的潜在成分，同时容纳了溢出效应和联动效应。通过采用特定的参数化方式，即使对于高维波动率向量，该模型在计算上仍然可行。为减少未知系数的数量，我们提出了一种基于模型的简单聚类程序。我们通过对道琼斯工业平均指数的29种资产进行实证应用，说明了所提方法的有效性，并深入揭示了波动率溢出效应与共享市场动态。与包括所提模型完全参数化版本在内的其他向量MEM进行对比分析表明，该模型在多项评估标准上均表现出优越或至少可比的性能。