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Asset Allocation Strategies Based On Penalized Quantile Regression

Author

Listed:
  • Giovanni Bonaccolto

    (University of Padova)

  • Massimiliano Caporin

    (University of Padova)

  • Sandra Paterlini

    (European Business School)

Abstract

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.

Suggested Citation

  • Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2015. "Asset Allocation Strategies Based On Penalized Quantile Regression," "Marco Fanno" Working Papers 0199, Dipartimento di Scienze Economiche "Marco Fanno".
  • Handle: RePEc:pad:wpaper:0199
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    References listed on IDEAS

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    Cited by:

    1. Taras Bodnar & Mathias Lindholm & Erik Thorsén & Joanna Tyrcha, 2021. "Quantile-based optimal portfolio selection," Computational Management Science, Springer, vol. 18(3), pages 299-324, July.
    2. Giovanni Bonaccolto, 2019. "Critical Decisions for Asset Allocation via Penalized Quantile Regression," Papers 1908.04697, arXiv.org.
    3. Giovanni Bonaccolto, 2021. "Quantile– based portfolios: post– model– selection estimation with alternative specifications," Computational Management Science, Springer, vol. 18(3), pages 355-383, July.
    4. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.

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    More about this item

    Keywords

    Quantile regression; l1-norm penalty; pessimistic asset allocation.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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