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A Jump Diffusion Model for Agricultural Commodities with Bayesian Analysis

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  • Adam Schmitz
  • Zhiguang Wang
  • Jung‐Han Kimn

Abstract

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

Suggested Citation

  • Adam Schmitz & Zhiguang Wang & Jung‐Han Kimn, 2014. "A Jump Diffusion Model for Agricultural Commodities with Bayesian Analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(3), pages 235-260, March.
    • Handle: RePEc:wly:jfutmk:v:34:y:2014:i:3:p:235-260
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    Cited by:

    1. Özbekler, Ali Gencay & Kontonikas, Alexandros & Triantafyllou, Athanasios, 2021. "Volatility forecasting in European government bond markets," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1691-1709.
    2. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    3. Kam Fong Chan & Philip Gray, 2017. "Do Scheduled Macroeconomic Announcements Influence Energy Price Jumps?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(1), pages 71-89, January.
    4. Diewald, Laszlo & Prokopczuk, Marcel & Wese Simen, Chardin, 2015. "Time-variations in commodity price jumps," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 72-84.
    5. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    6. Mengmeng Wang & Xue Fan, 2021. "An Empirical Study on How Livestreaming Can Contribute to the Sustainability of Green Agri-Food Entrepreneurial Firms," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
    7. Chih-Chen Hsu & An-Sing Chen & Shih-Kuei Lin & Ting-Fu Chen, 2017. "The affine styled-facts price dynamics for the natural gas: evidence from daily returns and option prices," Review of Quantitative Finance and Accounting, Springer, vol. 48(3), pages 819-848, April.
    8. Wu, Feng & Myers, Robert J. & Guan, Zhengfei & Wang, Zhiguang, 2015. "Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 260-274.
    9. Jang, H. & Lee, J., 2019. "Machine learning versus econometric jump models in predictability and domain adaptability of index options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 74-86.

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