An Asynchronous Regime Switching GO GARCH Model for Optimal Futures Hedging
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In this paper, an asynchronous Markov regime switching generalized orthogonal GARCH (ARSGO) model for optimal futures hedging is proposed. The proposed ARSGO is a regime switching GO GARCH such that different financial variables are governed by different state variables with the dependence of switching captured by a synchronization factor. Different from the conventional single-state-variable regime switching GO GARCH (RSGO), the multiple-state-variable ARSGO is more flexible in capturing the time-varying state-dependent correlation between spot and futures returns. ARSGO is applied to TAIEX futures to cross hedge the spot exposure of Taiwan stock sector indices. The empirical results reveal that the hedging effectiveness of ARSGO is superior to its nested models including the state-independent generalized orthogonal GARCH (GO) and the conventional single-state-variable RSGO models in terms of variance reductions and utility gains.
Abstract
In this paper, an asynchronous Markov regime switching generalized orthogonal GARCH (ARSGO) model for optimal futures hedging is proposed. The proposed ARSGO is a regime switching GO GARCH such that different financial variables are governed by different state variables with the dependence of switching captured by a synchronization factor. Different from the conventional single-state-variable regime switching GO GARCH (RSGO), the multiple-state-variable ARSGO is more flexible in capturing the time-varying state-dependent correlation between spot and futures returns. ARSGO is applied to TAIEX futures to cross hedge the spot exposure of Taiwan stock sector indices. The empirical results reveal that the hedging effectiveness of ARSGO is superior to its nested models including the state-independent generalized orthogonal GARCH (GO) and the conventional single-state-variable RSGO models in terms of variance reductions and utility gains.
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