Autoregressive Conditional Skewness
提出一种估计时变条件偏度的新方法，允许均值和方差变化，使用非中心t分布和最大似然估计，应用于股票回报数据发现条件偏度很重要，且与方差非对称性一致。
We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.