IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v74y2023ics0927539823001007.html
   My bibliography  Save this article

The commodity risk premium and neural networks

Author

Listed:
  • Rad, Hossein
  • Low, Rand Kwong Yew
  • Miffre, Joëlle
  • Faff, Robert

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

  • Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:empfin:v:74:y:2023:i:c:s0927539823001007
    DOI: 10.1016/j.jempfin.2023.101433
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539823001007
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2023.101433?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:empfin:v:74:y:2023:i:c:s0927539823001007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jempfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.