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

Lucio Fernandez Arjona and Damir Filipović
Additional contact information
Lucio Fernandez Arjona: Zurich Insurance Group
Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute

No 20-28, Swiss Finance Institute Research Paper Series from 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.

Keywords: Solvency capital; dimensionality reduction; neural networks; nested Monte Carlo; replicating portfolios. (search for similar items in EconPapers)
Pages: 36 pages
Date: 2020-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-gen and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3588376 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2028

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().