Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874356 | Journal of Computational Science | 2018 | 39 Pages |
Abstract
In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo simulations.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Gelin Gao, Bud Mishra, Daniele Ramazzotti,