Autoregressive conditional skewness

We present a new methodology for estimating time-varying conditional skewne ss. Our model allows for changing means and variances, uses a maximum Likel ihood 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 ev idence of asymmetric variance is consistent with conditional skewness. Incl usion of conditional skewness also impacts the persistence in conditional v ariance.