标题： 基于机器学习的印度金融市场投资组合压力测试框架 显示英文标题

标题： Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios

摘要： 本文提出了一种基于机器学习的框架，用于印度金融市场的部门压力测试，重点关注金融服务、信息技术、能源、消费品和制药行业。 最初，我们通过主成分分析和自编码器进行降维和潜在因子建模，解决了传统压力测试中的局限性。 在此基础上，我们通过变分自编码器扩展了该方法，为潜在空间引入了概率结构。 这使得基于蒙特卡洛的场景生成成为可能，从而能够更细致地、考虑分布的模拟压力市场条件。 所提出的框架能够捕捉复杂的非线性依赖关系，并通过风险价值和预期损失进行风险估计。 这些管道共同展示了机器学习方法在提高金融压力测试的灵活性、稳健性和现实性方面的潜力。 显示英文摘要

摘要： 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.