Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908431407906816 |
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| author | G, Vidya Sagar Ali, Shifat Chakrabarty, Siddhartha P. |
| author_facet | G, Vidya Sagar Ali, Shifat Chakrabarty, Siddhartha P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios G, Vidya Sagar Ali, Shifat Chakrabarty, Siddhartha P. Risk Management Machine Learning Portfolio Management 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. |
| title | Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios |
| topic | Risk Management Machine Learning Portfolio Management |
| url | https://arxiv.org/abs/2507.02011 |