On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging

While dynamic optimal hedging is of major interest, it remains unclear as to whether incorporating higher moments of a return distribution leads to better hedging decisions. We examine the effects of introducing a bivariate skew-Student density function with static and autoregressive conditional skewness and kurtosis on dynamic minimum-variance hedging strategies. Static higher order moments improve reductions in variance and value at risk of hedged portfolios. The inclusion of dynamics through an autoregressive component extends these improvements further. These benefits avail for short and long hedging horizons, which is highlighted in the global financial crisis. The static and conditional higher order moments enhance the notion that the size and smoothness of hedge ratios positively relate to hedging effectiveness while volatility does the reverse. Improved effectiveness can be explained given an upgrade of size and smoothness and a downgrade of volatility of hedge ratios attributed to the dynamics of higher order moments.