respectively. The financial support from Agricultural Experiment Station at South Dakota State University (Project H363-10) and Stahly Scholar in Financial Economics are gratefully acknowledged by Wang. We appreciate the comments by Wade Brorsen and Philip Garcia and other participants at the NCCC-134 conference. 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.

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 results for corn, soybeans and wheat generally, with few exceptions, lend support for the SVJ model. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:235–260, 2014

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