Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios

This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using V ariational Autoencoders, which introduces a probabilistic structure to the latent space. This enables Monte Carlo-based scenario generation, allowing for more nuanced, distribution-aware simulation of stressed market conditions. The proposed framework captures complex non-linear dependencies and supports risk estimation through V alue-at-Risk and Expected Shortfall. Together, these pipelines demonstrate the potential of Machine Learning approaches to improve the flexibility, robustness, and realism of financial stress testing. Stress testing is a fundamental component of financial risk management, designed to evaluate the resilience of financial systems or portfolios under adverse market conditions [1]. By simulating extreme but plausible scenarios, stress tests provide insight into potential vulnerabilities, that may not be evident during normal periods of market operation. Regulatory institutions such as the Basel Committee on Banking Supervision (BCBS) [2] and the Reserve Bank of India (RBI) [3] have emphasized the critical role of stress testing in ensuring financial stability, particularly in the aftermath of global financial crises of 2008.