International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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GANs for Scenario Analysis and Stress Testing in Financial Institutions

Author(s) Adarsh Naidu
Country United States
Abstract This study investigates the utilization of Generative Adversarial Networks (GANs) in constructing robust and realistic stress-testing scenarios for financial institutions. Stress testing has emerged as a pivotal regulatory necessity and risk management instrument in the wake of the 2008 financial crisis (Allen & Carletti, 2010) [1]. Conventional methods primarily rely on historical data or expert insights, which might not adequately account for novel but plausible crises (Basel Committee on Banking Supervision, 2018) [2]. We introduce a groundbreaking framework that employs GANs to generate a diverse set of realistic stress scenarios, addressing the deficiencies of traditional methodologies. Our empirical findings indicate that GAN-derived scenarios can replicate extreme market conditions while ensuring internal coherence across various economic indicators. This proposed approach fortifies financial institutions by enabling them to anticipate and mitigate a wider range of potential financial disruptions than what historical data alone can provide. Extensive trials using real-world financial datasets reveal that our framework surpasses traditional methods in scenario realism and risk coverage metrics, offering financial entities a more robust tool for assessing systemic vulnerabilities.
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-08
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.40681
Short DOI https://doi.org/g9bthb
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E-ISSN 2582-2160
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