Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios

ArXiv ID: 2507.02011 “View on arXiv”

Authors: Vidya Sagar G, Shifat Ali, Siddhartha P. Chakrabarty

Abstract

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.

Keywords: stress testing, Variational Autoencoder, Monte Carlo simulation, Principal Component Analysis, Expected Shortfall, equities (sectoral)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper utilizes advanced machine learning concepts like Variational Autoencoders and probabilistic modeling, indicating high mathematical complexity, while providing a concrete implementation pipeline with specific risk metrics (VaR, ES) and rolling-window methodology, demonstrating strong empirical rigor.

  flowchart TD
    A["Research Goal: Develop ML Framework<br>for Indian Financial Sector Stress Testing"] --> B["Data: Sectoral Equity Indices<br>for 5 Key Sectors"]
    
    B --> C{"ML Architecture"}
    
    C --> D["Pipeline 1: PCA & AE<br>Dimensionality Reduction"]
    C --> E["Pipeline 2: VAE<br>Probabilistic Latent Space"]
    
    D --> F["Scenario Generation"]
    E --> G["Monte Carlo Simulation<br>from VAE Latent Distribution"]
    
    F --> H["Outcome: Risk Metrics<br>VaR & Expected Shortfall"]
    G --> H
    
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style H fill:#bbf,stroke:#333,stroke-width:2px