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Ensemble learning for portfolio valuation and risk management

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  • Lotfi Boudabsa
  • Damir Filipovi'c

Abstract

We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. The method can also be applied to Bermudan style options. Numerical experiments show good results in moderate dimension problems.

Suggested Citation

  • Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    • Handle: RePEc:arx:papers:2204.05926
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    References listed on IDEAS

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