Variance (Non) Causality in Multivariate GARCH
研究了多元GARCH模型中方差非因果性的系数约束，并提出了指数因果GARCH模型，通过乘性因果影响函数直接解释方差因果效应，实证发现意大利股指期货收益率与交易量之间存在负向因果效应。
This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH. By the introduction of a multiplicative causality impact function, the variance causality effects becomes directly interpretable and can therefore be used to detect both the existence of causality and its direction; notably, the proposed model allows for increasing and decreasing variance effects. An empirical application evidences negative causality effects between returns and volume of an Italian stock market index future contract.