Genai-Driven Scenario Generation For Intraday Market Risk, Liquidity Stress Testing, And Portfolio Optimization
Keywords:
Generative Adversarial Networks, Intraday market risk, Liquidity stress testing, Portfolio optimization, Deep learning architectures, Scenario generation, Value at Risk, Tail risk assessmentAbstract
Generative artificial intelligence represents a transformative paradigm in financial risk management, enabling unprecedented advances in scenario generation, stress testing, and portfolio optimization. This research synthesizes state-of-the-art methodologies in generative adversarial networks, recurrent neural networks, and deep reinforcement learning for addressing multidimensional challenges in intraday market risk assessment and liquidity management. Empirical validation across 2023 implementations demonstrates that GenAI-enhanced frameworks achieve 94.7 percent accuracy in risk prediction compared to 88.5 percent for traditional methods, while simultaneously improving tail-risk capture by 39.4 percent relative to conventional Monte Carlo simulations. Portfolio optimization leveraging GenAI-ensemble techniques yields Sharpe ratios of 1.356 and Sortino ratios of 1.987, substantially outperforming classical mean-variance approaches by 1.72x in risk-adjusted returns. The integration of conditional scenario generation with liquidity stress testing frameworks enables financial institutions to identify systemic vulnerabilities 18–24 hours prior to manifestation under extreme market conditions. Implementation costs ranging from $11.4 million to $13.5 million across 22-month deployment cycles yield positive return-on-investment within 14–24 months through risk mitigation and operational efficiency gains.
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