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Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Author

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  • Krzysztof Drachal

    (Faculty of Economic Sciences, University of Warsaw, 00-241 Warszawa, Poland)

  • Michał Pawłowski

    (Faculty of Economic Sciences, University of Warsaw, 00-241 Warszawa, Poland)

Abstract

This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.

Suggested Citation

  • Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    • Handle: RePEc:gam:jijfss:v:12:y:2024:i:2:p:34-:d:1366740
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    as
    1. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    2. Li, Raymond & Leung, Guy C.K., 2011. "The integration of China into the world crude oil market since 1998," Energy Policy, Elsevier, vol. 39(9), pages 5159-5166, September.
    3. Kunlapath Sukcharoen & David Leatham, 2018. "Analyzing Extreme Comovements in Agricultural and Energy Commodity Markets Using a Regular Vine Copula Method," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 193-201.
    4. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    5. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    6. Baomin Dong & Xuefeng Li & Boqiang Lin, 2010. "Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach," Review of Development Economics, Wiley Blackwell, vol. 14(3), pages 499-519, August.
    7. Atil, Ahmed & Lahiani, Amine & Nguyen, Duc Khuong, 2014. "Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices," Energy Policy, Elsevier, vol. 65(C), pages 567-573.
    8. Ouyang, Ruolan & Zhang, Xuan, 2020. "Financialization of agricultural commodities: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 381-389.
    9. Luciana Juvenal & Ivan Petrella, 2015. "Speculation in the Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 621-649, June.
    10. Rodrigo da Silva Souza & Leonardo B. de Mattos & João E. de Lima, 2021. "Commodity prices and the Brazilian real exchange rate," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3152-3172, April.
    11. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    12. Irwin, Scott H. & Sanders, Dwight R. & Merrin, Robert P., 2009. "Devil or Angel? The Role of Speculation in the Recent Commodity Price Boom (and Bust)," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 41(2), pages 377-391, August.
    13. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    14. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2018. "Fundamentals, speculation or macroeconomic conditions? Modelling and forecasting Arabica coffee prices," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(4), pages 583-615.
    15. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    16. Mu, Xiaoyi, 2007. "Weather, storage, and natural gas price dynamics: Fundamentals and volatility," Energy Economics, Elsevier, vol. 29(1), pages 46-63, January.
    17. repec:bla:rdevec:v:14:y:2010:i:s1:p:499-519 is not listed on IDEAS
    18. Shiu-Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 949-967, August.
    19. Eyup Dogan, 2016. "The Relationship between Economic Growth, Energy Consumption and Trade," Bulletin of Energy Economics (BEE), The Economics and Social Development Organization (TESDO), vol. 4(1), pages 70-80, March.
    20. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    21. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    22. Kaufmann, Robert K., 2011. "The role of market fundamentals and speculation in recent price changes for crude oil," Energy Policy, Elsevier, vol. 39(1), pages 105-115, January.
    23. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    24. Xu, Yang & Han, Liyan & Wan, Li & Yin, Libo, 2019. "Dynamic link between oil prices and exchange rates: A non-linear approach," Energy Economics, Elsevier, vol. 84(C).
    25. Working, Holbrook, 1960. "Speculation on Hedging Markets," Food Research Institute Studies, Stanford University, Food Research Institute, vol. 1(2), pages 1-36.
    26. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    27. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    28. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    29. Scott C. Linn & Zhen Zhu, 2004. "Natural gas prices and the gas storage report: Public news and volatility in energy futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(3), pages 283-313, March.
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