Quantifying Systematic Financial Risk via Generative Adversarial Networks Synthesizing Correlated Extreme Market Scenarios for Enhanced Stress Testing Reliability
Abstract
The assessment of systematic financial risk remains a cornerstone of global economic stability, yet traditional methodologies often fail to capture the nonlinear dependencies and "fat-tail" distributions characteristic of modern market crises. Traditional stress testing frameworks typically rely on historical replay or simplistic parametric assumptions that do not account for the rapid shifts in asset correlations during periods of extreme volatility. This research explores a sophisticated computational approach using Generative Adversarial Networks (GANs) to synthesize highly realistic, correlated extreme market scenarios. By leveraging a dual-network architecture—where a generator creates synthetic financial time series and a discriminator evaluates their authenticity against historical distributions—the proposed system identifies hidden vulnerabilities in institutional portfolios that conventional models overlook. This paper provides an in-depth analysis of the structural trade-offs involved in deploying generative models within regulated financial infrastructures. We emphasize the transition from static stress testing to a dynamic, high-fidelity simulation environment that prioritizes system-level robustness and governance. Furthermore, the discussion extends to the policy implications of utilizing black-box generative models in auditing and the necessity of ensuring fairness and transparency in automated risk quantification. By bridging the gap between advanced machine learning and macro-prudential oversight, this study offers a comprehensive framework for enhancing the reliability of stress testing in an increasingly interconnected global economy.
References
1.Arner, D. W., Barberis, J., & Buckley, R. P. (2017). The evolution of Fintech: A new post-crisis paradigm? Georgetown Journal of International Law, 47(4), 1271-1319.
2.Avellaneda, M., & Levy, A. (1995). Pricing and hedging derivative securities in markets with uncertain volatilities. Applied Mathematical Finance, 2(2), 73-88.
3.Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 12(7), e0180944.
4.Borio, C. (2011). Implementing a macroprudential framework: Blending boldness and realism. Capitalism and Society, 6(1), Article 2.
5.Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223-236.
6.Danielsson, J., James, K. R., Valenzuela, M., & Zer, Y. (2016). Model risk of risk models. Journal of Financial Stability, 23, 79-91.
7.Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
8.Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3313-3332.
9.Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.
10.Hu, L., & Shen, Y. (2026). A predictive analytics approach for forecasting global stock index returns using deep learning techniques. Decision Analytics Journal, 100685.
11.Hull, J. C. (2018). Risk Management and Financial Institutions (5th ed.). Wiley.
12.Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125-1134.
13.Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
14.Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin.
15.Kondratyev, A. (2018). Learning curve dynamics with generative adversarial networks. SSRN Electronic Journal.
16.Krahbichler, P., & Schwendner, P. (2026). Financial scenario generation with generative adversarial networks. Journal of Computational Finance, 29(2), 45-72.
17.Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least-squares approach. The Review of Financial Studies, 14(1), 113-147.
18.Lo, A. W. (2012). Reading about the financial crisis: A twenty-one-book review. Journal of Economic Literature, 50(1), 151-178.
19.Mizuta, T. (2016). A review of financial market simulations by multi-agent systems: Using it for financial regulation and design. Evolutionary and Institutional Economics Review, 13(2), 341-353.
20.Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
21.Ni, L., Li, J. C., & Tang, S. (2026). GAN-based synthetic data generation for imbalanced financial fraud detection. Engineering Applications of Artificial Intelligence, 148, 108922.
22.Ozbayoglu, A. M., Gudelek, M. U., & Guner, O. B. (2020). Deep learning for financial applications: A comprehensive survey. Applied Soft Computing, 93, 106384.
23.Rajore, A., & Gupta, S. (2026). Assessing systematic risk using advanced neural architectures in volatile markets. Systems Research and Behavioral Science, 43(1), 112-130.
24.Riedel, C. (2026). The role of AI in macro-prudential oversight: Policy and implementation. Journal of Banking Regulation, 27(3), 201-218.
25.Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21-42.
26.Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training GANs. Advances in Neural Information Processing Systems, 29.
27.Schwarcz, S. L. (2008). Systemic risk. Georgetown Law Journal, 97, 193.
28.Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
29.Tarashev, N., Borio, C., & Tsatsaronis, K. (2010). Attributing systemic importance to individual institutions. BIS Quarterly Review, March, 47-60.
30.Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1(1), 15-29.
31.Wiese, M., Knobloch, R., Korn, R., & Kretschmer, P. (2020). Quant GANs: Deep generation of financial time series. Quantitative Finance, 20(9), 1419-1440.
32.Yoon, J., Jarrett, D., & van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in Neural Information Processing Systems, 32.
33.Zhang, Y., & Chen, X. (2026). Ethics and governance in the era of automated financial risk management. AI and Society, 41(2), 567-584.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
