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Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data

Author

Listed:
  • Malvina Marchese

    (City, University of London)

  • María Dolores Martínez-Miranda

    (University of Granada)

  • Jens Perch Nielsen

    (City, University of London)

  • Michael Scholz

    (University of Klagenfurt
    JOANNEUM RESEARCH Forschungsgesellschaft mbH)

Abstract

The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations. Empirical applications to annual financial returns and actuarial telematics data show its usefulness in the financial and insurance industries.

Suggested Citation

  • Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024. "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-16, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00657-9
    DOI: 10.1186/s40854-024-00657-9
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Forecasting; Non-linear prediction; Stock returns; Dimension reduction; Telematics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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