All in the family Nesting symmetric and asymmetric GARCH models
提出一个参数化GARCH模型族，能嵌套多种常见对称与非对称GARCH模型，便于检验不同非对称性和函数形式。用美国日度股票数据检验，发现标准GARCH模型均被拒绝，最优模型的条件标准差取决于冲击绝对值的三次方半幂和过去标准差。
This paper develops a parametric family of models of generalized autoregressive heteroskedasticity (GARCH). The family nests the most popular symmetric and asymmetric GARCH models, thereby highlighting the relation between the models and their treatment of asymmetry. Furthermore, the structure permits nested tests of different types of asymmetry and functional forms. Daily U.S. stock return data reject all standard GARCH models in favor of a model in which, roughly speaking, the conditional standard deviation depends on the shifted absolute value of the shocks raised to the power three halves and past standard deviations.