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The commodity risk premium and neural networks

Author

Listed:
  • Joelle Miffre

    (Audencia Business School, Louis Bachelier Fellow)

  • Hossein Rad

    (UQ Business School. The University of Queensland. Queensland 4072, Australia)

  • Rand Kwong Yew Low

    (Bond Business School)

  • Robert Faff

    (Bond Business School)

Abstract

The paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and the risk premium of commodity futures contracts. The linear models use shrinkage methods based on either naive averaging or principal components. The nonlinear models use feedforward deep neural networks (DNN) either as stand-alone or in conjunction with a long short-term memory network (LSTM). Out of the four specifications considered, the LSTM-DNN architecture best captures the risk premium, which underscores the need to estimate models that are both nonlinear and recurrent. The superior performance of the LSTM-DNN portfolio persists after accounting for transaction costs or illiquidity and is unrelated to previously-documented commodity risk factors.

Suggested Citation

  • Joelle Miffre & Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2023. "The commodity risk premium and neural networks," Post-Print hal-04322519, HAL.
    • Handle: RePEc:hal:journl:hal-04322519
    DOI: 10.1016/j.jempfin.2023.101433
    Note: View the original document on HAL open archive server: https://hal.science/hal-04322519
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    Keywords

    Recurrent neural network; Commodity risk premium; Macroeconomic and financial variables; Nonlinear and linear predictive models;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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