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RCML: A Novel Algorithm for Regressing Price Movement during Commodity Futures Stress Testing Based on Machine Learning

Author

Listed:
  • Caifeng Liu

    (Post-Doctoral Workstation, Dalian Commodity Exchange, Dalian 116000, China)

  • Wenfeng Pan

    (Futures Information Technology Co., Ltd., Dalian Commodity Exchange, Dalian 116000, China)

  • Hongcheng Zhou

    (Futures Information Technology Co., Ltd., Dalian Commodity Exchange, Dalian 116000, China)

Abstract

Stress testing, an essential part of the risk management toolkit of financial institutions, refers to the evaluation of a portfolio’s potential risk under an extreme, but plausible, scenario. The most representative method for performing stress testing is historical scenario simulation, which aims to evaluate historical adverse market events on the current portfolios of financial institutions. However, some current commodities were not listed in the commodity futures market at the time of the historical event, causing a lack of the necessary price information to revalue the current positions of these commodities. To avoid over reliance on human hypothesis for these non-existent commodity futures, we propose a novel approach, RCML, to infer reasonable price movements for commodities unlisted in historical events. Unlike the previous methods, based on subjective hypothesis, RCML takes advantage of not only machine learning algorithms, but also multi-view information. Back testing and hypothesis testing are adopted to prove the rationality of RCML results.

Suggested Citation

  • Caifeng Liu & Wenfeng Pan & Hongcheng Zhou, 2023. "RCML: A Novel Algorithm for Regressing Price Movement during Commodity Futures Stress Testing Based on Machine Learning," JRFM, MDPI, vol. 16(6), pages 1-12, May.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:6:p:285-:d:1155166
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    References listed on IDEAS

    as
    1. Pierre-Antoine Mudry & Florentina Paraschiv, 2016. "Stress-Testing for Portfolios of Commodity Futures with Extreme Value Theory and Copula Functions," Lecture Notes in Economics and Mathematical Systems, in: Raquel J. Fonseca & Gerhard-Wilhelm Weber & João Telhada (ed.), Computational Management Science, edition 1, pages 17-22, Springer.
    2. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
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