Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

Abstract

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross-interdependence between the upward range and the downward range. Regression test shows that the cross-interdependence cannot be explained by leverage effect. To include the cross-interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in-sample and out-of-sample forecasting.