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Forecasting the term structure of commodities future prices using machine learning

Author

Listed:
  • Mario Figueiredo

    (Instituto de Matemática Pura e Aplicada)

  • Yuri F. Saporito

    (Getulio Vargas Foundation)

Abstract

We consider the dynamic Nelson–Siegel and the Schwartz–Smith three-factor models for future price curves. We study the prediction of the 1-day ahead prices of the entire curve for each model, using two different approaches: ordinary least squares (OLS) and filtering. For the dynamic Nelson–Siegel, we test the random walk, VAR and models based on long short-term memory (LSTM), including a filtering approach using LSTM. For the Schwartz–Smith model, we use the equations given by no-arbitrage assumptions. We also test a model that tries to predict the prices directly from the curve, which we refer to as basic model. We exemplify our approach using Brent, WTI and Copper future prices. We show that the dynamic Nelson–Siegel was the best model for fitting the data, and that the LSTM and VAR methods under the OLS approach generated the best forecasts.

Suggested Citation

  • Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
    • Handle: RePEc:spr:digfin:v:5:y:2023:i:1:d:10.1007_s42521-022-00069-3
    DOI: 10.1007/s42521-022-00069-3
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    More about this item

    Keywords

    Future prices; Forecasting; Deep learning; Commodities; Dynamic Nelson–Siegel; Schwartz–Smith model;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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