Granger-causal analysis of GARCH models: A Bayesian approach
研究了多元GARCH模型中条件方差的格兰杰因果关系，推导了非因果假设的参数约束，并提出贝叶斯检验方法以避免经典Wald检验的奇异性问题。
A multivariate GARCH model is used to investigate Granger causality in the conditional variance of time series. Parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well, are derived. These novel conditions are convenient for the analysis of potentially large systems of economic variables. To evaluate hypotheses of noncausality, a Bayesian testing procedure is proposed. It avoids the singularity problem that may appear in the Wald test, and it relaxes the assumption of the existence of higher-order moments of the residuals required in classical tests.