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A penalized two-pass regression to predict stock returns with time-varying risk premia

Author

Listed:
  • Gaetan Bakalli

    (University of Geneva)

  • Stéphane Guerrier

    (University of Geneva)

  • Olivier Scaillet

    (University of Geneva and Swiss Finance Institute)

Abstract

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.

Suggested Citation

  • Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series 21-09, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2109
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    Cited by:

    1. Guillaume Coqueret & Martial Laguerre, 2025. "Overparametrized models with posterior drift," Papers 2506.23619, arXiv.org.
    2. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
    3. Neuhierl, Andreas & Tang, Xiaoxiao & Varneskov, Rasmus Tangsgaard & Zhou, Guofu, 2022. "Option characteristics as cross-sectional predictors," LawFin Working Paper Series 37, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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