用机器学习提升印度金融市场的压力测试精准度。
Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
- 通过自编码器与变分自编码器降维建模，捕捉市场非线性关系。
- 基于概率潜空间生成蒙特卡洛场景，实现分布感知的压力模拟。
- 适用于金融机构风险评估，尤其适合关注印度五大行业者。
本文提出一种基于机器学习的印度金融市场行业压力测试框架，涵盖金融服务、信息技术、能源、消费品和制药领域。针对传统压力测试的局限性，采用主成分分析与自编码器进行降维及潜在因子建模；进一步引入变分自编码器，在潜空间中构建概率结构，支持基于蒙特卡洛的场景生成，实现更细致、分布感知的市场压力模拟。该框架可有效捕捉复杂非线性依赖关系，并通过风险价值（Value-at-Risk）与预期缺口（Expected Shortfall）进行风险估计。整体方法显著提升了压力测试的灵活性、鲁棒性与现实性。
原文摘要 · Abstract (English)
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.
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