Jing Zhou, Dong Li, Rui Pan, Hansheng Wang

【Abstract】

The multivariate GARCH (MGARCH) model is popularly used for analyzing financial time series data. However, statistical inference for MGARCH models is quite challenging due to the high dimension issue. To overcome this difficulty, we propose a network GARCH model. The newly proposed model makes use of information derived from an appropriately defined network structure. By doing so, the number of unknown parameters is highly decreased, and the computational complexity is substantially reduced. Strict and weak stationarity of the network GARCH model is rigorously established. In order to estimate the model, a quasi-maximum likelihood estimator (QMLE) is developed, and its asymptotic properties are investigated. Simulation studies are carried out to assess the performance of the QMLE in finite samples and empirical examples are analyzed to illustrate the usefulness of network GARCH models.

【Keywords】

        GARCH Model, Multivariate GARCH Model, network, structure, Quasi-Maximum Likelihood Estimator.