A Jump Diffusion Model for Agricultural Commodities with Bayesian Analysis

Stochastic volatility, price jumps, seasonality, and stochastic cost of carry, have been included separately, but not collectively, in pricing models of agricultural commodity futures and options. We propose a comprehensive model that incorporates all four features. We employ a special Markov Chain Monte Carlo algorithm, new in the agricultural commodity derivatives pricing literature, to estimate the proposed stochastic volatility (SV) and stochastic volatility with jumps (SVJ) models. Overall model fitness tests favor the SVJ model. The in-sample and out-of-sample pricing and hedging results for corn, soybeans and wheat generally, with few exceptions, lend support for the SVJ model.