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...
