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Risky times: Seasonality and event risk of commodities

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  • Dominik Boos

Abstract

The seasonal risk of wheat, corn, and soybean is modeled by a novel seasonality filter based on a generalized ridge regression. Then, using a component GARCH model, seasonal risk is combined with event risk and a short‐term risk dynamics. The resulting model is robust, generates seasonal patterns related to the crop cycle, and significantly outperforms the standard GARCH(1,1) in terms of out‐of‐sample risk prediction. Results are relevant for risk management and portfolio construction.

Suggested Citation

  • Dominik Boos, 2024. "Risky times: Seasonality and event risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 767-783, May.
    • Handle: RePEc:wly:jfutmk:v:44:y:2024:i:5:p:767-783
    DOI: 10.1002/fut.22492
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    1. Isengildina-Massa, Olga & Cao, Xiang & Karali, Berna & Irwin, Scott H. & Adjemian, Michael & Johansson, Robert C., 2021. "When does USDA information have the most impact on crop and livestock markets?," Journal of Commodity Markets, Elsevier, vol. 22(C).
    2. Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L. & Gomez, Jennifer K., 2008. "The Impact of Situation and Outlook Information in Corn and Soybean Futures Markets: Evidence from WASDE Reports," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 89-103, April.
    3. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    4. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    5. Constantino Hevia & Ivan Petrella & Martin Sola, 2018. "Risk premia and seasonality in commodity futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 853-873, September.
    6. Back, Janis & Prokopczuk, Marcel & Rudolf, Markus, 2013. "Seasonality and the valuation of commodity options," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 273-290.
    7. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    8. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    9. Andrew M. McKenzie & Michael R. Thomsen & Michael K. Adjemian, 2022. "Characterizing implied volatility functions from agricultural options markets," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1605-1624, October.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Karali, Berna & Thurman, Walter N., 2010. "Components of Grain Futures Price Volatility," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(2), pages 1-16, August.
    12. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    13. Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian & Sebastien Mcmahon, 2008. "Forecasting commodity prices: GARCH, jumps, and mean reversion," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 279-291.
    14. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    15. Dorfmann, Jeffrey & Karali, Berna, 2015. "A Nonparametric Search for Information Effects from USDA Reports," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(1), pages 1-20.
    16. Michael McKenzie & Heather Mitchell & Robert Brooks & Robert Faff, 2001. "Power ARCH modelling of commodity futures data on the London Metal Exchange," The European Journal of Finance, Taylor & Francis Journals, vol. 7(1), pages 22-38.
    17. Arismendi, Juan C. & Back, Janis & Prokopczuk, Marcel & Paschke, Raphael & Rudolf, Markus, 2016. "Seasonal Stochastic Volatility: Implications for the pricing of commodity options," Journal of Banking & Finance, Elsevier, vol. 66(C), pages 53-65.
    18. McKenzie, Andrew M. & Ke, Yangmin, 2022. "How do USDA announcements affect international commodity prices?," Journal of Commodity Markets, Elsevier, vol. 28(C).
    19. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    20. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
    21. Chen, Yu-Fu & Mu, Xiaoyi, 2021. "Asymmetric volatility in commodity markets," Journal of Commodity Markets, Elsevier, vol. 22(C).
    22. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    23. Aït-Sahalia, Yacine & Mancini, Loriano, 2008. "Out of sample forecasts of quadratic variation," Journal of Econometrics, Elsevier, vol. 147(1), pages 17-33, November.
    24. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    25. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    26. Boos, Dominik & Grob, Linus, 2023. "Tracking speculative trading," Journal of Financial Markets, Elsevier, vol. 64(C).
    27. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    28. David P. Simon, 2002. "Implied volatility forecasts in the grains complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(10), pages 959-981, October.
    29. Andrew M. McKenzie, 2008. "Pre-Harvest Price Expectations for Corn: The Information Content of USDA Reports and New Crop Futures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(2), pages 351-366.
    30. Geman, Hélyette & Smith, William O., 2013. "Theory of storage, inventory and volatility in the LME base metals," Resources Policy, Elsevier, vol. 38(1), pages 18-28.
    31. Olga Isengildina-Massa & Scott H. Irwin & Darrel L. Good & Jennifer K. Gomez, 2008. "Impact of WASDE reports on implied volatility in corn and soybean markets," Agribusiness, John Wiley & Sons, Ltd., vol. 24(4), pages 473-490.
    32. Symeonidis, Lazaros & Prokopczuk, Marcel & Brooks, Chris & Lazar, Emese, 2012. "Futures basis, inventory and commodity price volatility: An empirical analysis," Economic Modelling, Elsevier, vol. 29(6), pages 2651-2663.
    33. Michael K. Adjemian, 2012. "Quantifying the WASDE Announcement Effect," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 238-256.
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