Risk Management Strategies for Commodity Processors

Abstract

Recent years have witnessed an increase in agricultural commodity price volatility. This thesis analyzes different models to derive optimal hedge strategies for commodity processors, with two components addressed. One is the dependence structure and joint distribution among inputs, outputs, and hedging instruments that impact hedging effectiveness. The second refers to different procurement and sales scenarios a processor may encounter. A domestic flour mill company is used to demonstrate alternative hedging strategies under different processing scenarios. Copula is a relatively new method used to capture flexible dependence structure and joint distribution among assets. The applications of copulas in the agricultural literature are recent. This thesis integrates the concept of copula and widely studied risk measurement Value at Risk (VaR) to derive the optimal risk management strategy. Mean-VaR with copula calculation is shown to be an efficient and confident approach to analyze empirical studies.