Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
This provides a more realistic stress testing method for financial risk managers in India, though it is incremental as it builds on existing ML techniques.
The paper tackles the limitations of conventional stress testing in the Indian financial market by developing a machine learning framework that uses Variational Autoencoders for probabilistic latent modeling and Monte Carlo scenario generation, resulting in improved flexibility, robustness, and realism for risk estimation through Value-at-Risk and Expected Shortfall.
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 Variational 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 Value-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.