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Conditional autoencoder pricing model for energy commodities

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  • Liu, Zhenya
  • Teka, Hanen
  • You, Rongyu

Abstract

We propose a conditional latent factor asset pricing model for energy commodities (CAE) that uses a modified conditional autoencoder neural network to capture the non-linear relationship between latent factors and factor loadings. In addition to spot prices, we incorporate 127 macroeconomic and 598 energy information characteristics to extract the factor loadings. The empirical results demonstrate the high-quality performance of the model in out-of-sample testing. Furthermore, by analyzing characteristic importance, we find that energy information characteristics, particularly coal, electricity, and crude oil and natural gas resource development, play a dominant role in explaining the excess returns of energy commodities.

Suggested Citation

  • Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023. "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, vol. 86(PA).
    • Handle: RePEc:eee:jrpoli:v:86:y:2023:i:pa:s0301420723007717
    DOI: 10.1016/j.resourpol.2023.104060
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    More about this item

    Keywords

    Energy commodity; Conditional autoencoder; Machine learning; Neural network; Big data;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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