This paper develops a flexible and computationally efficient multivariate volatility model, which allows for dynamic conditional correlations and volatility spillover effects among financial assets. The new model has desirable properties such as identifiability and computational tractability for many assets. A sufficient condition of the strict stationarity is derived for the new process. Two quasi-maximum likelihood estimation methods are proposed for the new model with and without low-rank constraints on the coefficient matrices respectively, and the asymptotic properties for both estimators are established. Moreover, a Bayesian information criterion with selection consistency is developed for order selection, and the testing for volatility spillover effects is carefully discussed. The finite sample performance of the proposed methods is evaluated in simulation studies for small and moderate dimensions. The usefulness of the new model and its inference tools is illustrated by two empirical examples for 5 stock markets and 17 industry portfolios, respectively.

翻译：本文提出了一种灵活且计算高效的多元波动率模型，该模型能够刻画金融资产之间的动态条件相关性和波动溢出效应。新模型具有可识别性和计算可行性等优良性质，适用于大规模资产组合。我们推导了新过程严格平稳性的充分条件，并针对系数矩阵是否施加低秩约束的两种情形分别提出了拟极大似然估计方法，建立了两种估计量的渐近性质。同时，开发了具有选择一致性的贝叶斯信息准则用于阶数选择，并深入探讨了波动溢出效应的检验方法。通过小维度和中维度下的模拟研究评估了所提方法的有限样本性能，并分别以5个股票市场和17个行业组合为实证案例，展示了新模型及其推断工具的应用价值。