State-dependent hedge strategy for crude oil spot and futures markets

Abstract

Relying on the hidden Markov model improved by the particle swarm optimization algorithm (PSO-HMM), we develop a dual-decision method to address the issue of state-dependent futures hedging. Our approach is attractive in two ways. First, it uses the PSO algorithm to overcome the shortcomings of the traditional algorithm, which can easily fall into the local optima to estimate parameters in a hidden Markov mode. Second, this paper proposes a new hedge position adjustment method based on the identified market states, instead of sticking to the hedge position calculated by the commonly used GARCH-type models to achieve a better trade-off between risk hedging and return acquisition. Specifically, we first improve the accuracy of parameter measurement and employ the PSO-HMM to identify two market states, bear and bull, and fully illustrate the rationality and effectiveness of the proposed model. Based on the market states identified, we then adjust the hedge ratio estimated by GARCH-type models and compare the hedging effects of no hedge, model-driven, and state-dependent strategies. Our empirical results show that the PSO-HMM method can improve the accuracy of state identification over the classical HMM. The market state-dependent hedging strategy has better performance than other strategies when it comes to the trade-off between the return and the risk of a hedged portfolio. Furthermore, robustness checks under different conditions confirm that the state-dependent hedging strategy outperforms the model-driven hedging and no hedge strategies. Thus, our research sheds new light on conventional hedging models.