Research on optimization strategy of futures hedging dependent on market state

Abstract

Considering the dynamic nature of market conditions, this paper introduces a state-dependent futures hedging optimization model and methodology. This approach dynamically adjusts the traditional model-driven hedging strategy, effectively balancing the pursuit of returns with the imperative of risk mitigation. Empirical evidence shows that integrating Hidden Markov Model (HMM) with machine learning techniques, as demonstrated in this study, improves the accuracy of market state forecasts. Compared to the traditional model-driven hedging strategy, the innovative state-dependent hedging strategy introduced here significantly enhances the return-to-risk ratio, and revenue, without increasing hedging risks. Moreover, the hedging portfolio developed under this strategy achieves an average hedging efficiency of 0.76, highlighting the effectiveness of the proposed methodology. Additional robustness tests indicate that this market state-dependent hedging optimization strategy is promising under various conditions, including different position adjustment ratios, volatility benchmarks, evaluation periods, types of crude oil, and transaction costs. The research conducted in this paper not only contributes to and expands traditional hedging theories but also provides a practical risk management solution for market participants.