As an environmental protection fuel, methanol is widely used in national economy. The tremendous price fluctuation of methanol makes risk avoidance an important issue. However, traditional normal hypothesis in the existing literature underestimates the potential risk and leads to an inefficient hedging strategy, so we studied hedging strategy with methanol futures contracts based on the skewed normal hypothesis. Considering that copula methods allow us to construct a flexible multivariate distribution when solving the problem of asymmetry and nonlinearity, the dependence structure between spot and futures return is modelled through copula functions in this paper. Since likelihood equations do not have explicit solutions in the context of skewed normal, Genetic Algorithm is used to estimate the parameters of a skew normal distribution. To deal with the complexity of the proposed model, the artificial bee colony algorithm is adopted to search for the optimal solutions. Empirical results show that skewed normal distribution can represent the distribution characteristics of return better and improve the hedging effectiveness. Gaussian copula describes the dependence structure of spot and futures quite well. The algorithms designed to obtain the parameters in the marginal distributions and to find the optimal hedge ratio are effective and feasible.

甲醇作为一种环保燃料，在国民经济中得到广泛应用。甲醇价格的巨大波动使规避风险成为一个重要问题。然而，现有文献中传统的正态假设低估了潜在风险，导致套期保值策略效率低下，因此我们基于偏态正态假设研究甲醇期货合约的套期保值策略。考虑到copula方法在解决不对称性和非线性问题时可以构建灵活的多元分布，本文通过copula函数对现货和期货收益之间的依赖结构进行建模。由于似然方程在偏态正态的上下文中没有明确的解，遗传算法用于估计偏态正态分布的参数。针对所提出模型的复杂性，采用人工蜂群算法寻找最优解。实证结果表明，偏态正态分布能够更好地代表收益的分布特征，提高套期保值的有效性。Gaussian copula 很好地描述了现货和期货的依赖结构。旨在获取边际分布中的参数并找到最佳对冲比率的算法是有效且可行的。Gaussian copula 很好地描述了现货和期货的依赖结构。旨在获取边际分布中的参数并找到最佳对冲比率的算法是有效且可行的。Gaussian copula 很好地描述了现货和期货的依赖结构。旨在获取边际分布中的参数并找到最佳对冲比率的算法是有效且可行的。