Hedging Salmon Price Risk Based on Fuzzy Copula-GMM Model

Abstract

Copula method can explain the dependent function or connection function which connects the joint distribution and the univariate marginal distribution. Therefore, copula has recently become a most significant important tool in the financial field of risk management, portfolio allocation, and derivative asset pricing. However, it leads to a possibilistic uncertainty in estimating the parameters of copulas because of insufficient historical data, imprecise parameter estimation, and uncertain knowledge of future prices. This paper proposes a fuzzy copula model via Kullback–Leibler (KL) divergence to model the fuzzy relations, and further to investigate the hedging issues of salmon futures. We use a new framework of hedging under fuzzy circumstances, consisting of innovative marginal distributions and fuzzy intervals. By synergizing fuzzy copula and simulations, we use the fuzzy copula-GMM to obtain the hedge ratios of salmon futures. The empirical results show that, compared with traditional probabilistic methods, the fuzzy copula-GMM hedges the salmon spot risk measured by variance more successfully.