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Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques

Author

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  • Oleksandr Castello

    (School of Social Sciences, Department of Economics and Business Studies, University of Genova, 16126 Genova, Italy
    These authors contributed equally to this work.)

  • Marina Resta

    (School of Social Sciences, Department of Economics and Business Studies, University of Genova, 16126 Genova, Italy
    These authors contributed equally to this work.)

Abstract

We compare parametric and machine learning techniques (namely: Neural Networks) for in–sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende–Ferreira five–factor model with time–varying decay parameters and a Feed–Forward Neural Network to the bond market data of the BRICS countries. To enhance the flexibility of the parametric model, we also introduce a new procedure to estimate the time varying parameters that significantly improve its performance. Our contribution spans towards two directions. First, we offer a comprehensive investigation of the bond market in the BRICS countries examined both by time and maturity; working on five countries at once we also ensure that our results are not specific to a particular data–set; second we make recommendations concerning modelling and estimation choices of the yield curve. In this respect, although comparing highly flexible estimation methods, we highlight superior in–sample capabilities of the neural network in all the examined markets and then suggest that machine learning techniques can be a valid alternative to more traditional methods also in presence of marked turbulence.

Suggested Citation

  • Oleksandr Castello & Marina Resta, 2022. "Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques," Risks, MDPI, vol. 10(2), pages 1-18, February.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:2:p:36-:d:743852
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    References listed on IDEAS

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    Cited by:

    1. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.

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