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Machine Learning and Risk Management: SVDD Meets RQE

Author

Listed:
  • Dionne, Georges

    (HEC Montreal, Canada Research Chair in Risk Management)

  • Koumou, Gilles Boevi

    (HEC Montreal, Canada Research Chair in Risk Management)

Abstract

This paper re-examines Rao’s Quadratic Entropy (RQE) portfolio diversification in the light of the support vector data description (SVDD), an unsupervised machine learning algorithm developed for the one-class classification. It makes the link between RQE portfolio diversification and the SVDD. More specifically, we show that RQE portfolio diversification problem is equivalent to the dual representation of a hard-margin SVDD problem. This result demonstrates, on the one hand, that the SVDD and its rich set of extensions are relevant for risk management, in particular portfolio diversification. On the second hand, it provides new insights on RQE portfolio diversification approach in terms of interpretation, understanding, implementation, specification, and optimization. This strengthens the believe that machine learning can play an important role in risk management and RQE is an adequate framework to quantify and to manage portfolio diversification.

Suggested Citation

  • Dionne, Georges & Koumou, Gilles Boevi, 2018. "Machine Learning and Risk Management: SVDD Meets RQE," Working Papers 18-6, HEC Montreal, Canada Research Chair in Risk Management.
    • Handle: RePEc:ris:crcrmw:2018_006
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    References listed on IDEAS

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    More about this item

    Keywords

    Machine Learning; One-Class Classification; Support Vector Data Description; Rao’s Quadratic Entropy; Portfolio Diversification;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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