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Seasonal volatility in agricultural markets: modelling and empirical investigations

Author

Listed:
  • L. Schneider

    (EMLYON Business School)

  • B. Tavin

    (EMLYON Business School)

Abstract

This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson effect. We give the conditions under which the volatility dynamics are well defined and obtain the joint characteristic function of a pair of futures prices. We then derive the state-space representation of our model in order to use the Kalman filter algorithm for estimation and prediction. The empirical analysis is carried out using daily futures data from 2007 to 2019 for corn, cotton, soybeans, sugar and wheat. In-sample, the seasonal models clearly outperform the nested non-seasonal models in all five markets. Out-of-sample, we predict volatility peaks with high accuracy for four of these five commodities.

Suggested Citation

  • L. Schneider & B. Tavin, 2024. "Seasonal volatility in agricultural markets: modelling and empirical investigations," Annals of Operations Research, Springer, vol. 334(1), pages 7-58, March.
    • Handle: RePEc:spr:annopr:v:334:y:2024:i:1:d:10.1007_s10479-021-04241-7
    DOI: 10.1007/s10479-021-04241-7
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    More about this item

    Keywords

    Stochastic volatility; Model selection; Agricultural commodities; Seasonal volatility;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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