A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference

Abstract

This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.