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A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Author

Listed:
  • Lucio Fernandez Arjona

    (Zurich Insurance Group)

  • Damir Filipović

    (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute)

Abstract

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios.

Suggested Citation

  • Lucio Fernandez Arjona & Damir Filipović, 2020. "A machine learning approach to portfolio pricing and risk management for high-dimensional problems," Swiss Finance Institute Research Paper Series 20-28, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2028
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    Keywords

    Solvency capital; dimensionality reduction; neural networks; nested Monte Carlo; replicating portfolios.;
    All these keywords.

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