An efficient multivariate volatility model for many assets
arxiv(2024)
摘要
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.
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