A machine learning approach to portfolio pricing and risk management for high-dimensional problems
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.14149
- arXiv:
- arXiv:2004.14149
- Bibcode:
- 2020arXiv200414149F
- Keywords:
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- Quantitative Finance - Risk Management;
- Quantitative Finance - Computational Finance
- E-Print:
- 26 pages (main), 13 pages (appendix), 3 figures, 20 tables