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Optimal and robust combination of forecasts via constrained optimization and shrinkage


  • Roccazzella, Francesco
  • Gambetti, Paolo
  • Vrins, Frédéric


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  • Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2020. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Discussion Papers LFIN 2020006, Université catholique de Louvain, Louvain Finance (LFIN).
  • Handle: RePEc:ajf:louvlf:2020006

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    References listed on IDEAS

    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    3. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    4. Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
    5. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
    6. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
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    Cited by:

    1. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "High Dimensional Forecast Combinations Under Latent Structures," Papers 2010.09477,
    2. Gambetti, Paolo & Roccazzella, Francesco & Vrins, Frédéric, 2020. "Meta-learning approaches for recovery rate prediction," LIDAM Discussion Papers LFIN 2020007, Université catholique de Louvain, Louvain Finance (LFIN).

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    forecasts combination ; robust methods ; optimal combination ; machine learning;
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