Research on the Application of Machine Learning in VaR/CVaR Measurement of Market, Credit, and Operational Risks

This study explores the integration of machine learning techniques in measuring market, credit, and operational value-at-risk (VaR) and conditional value-at-risk (VaR). By integrating traditional tools such as historical simulation method and Monte Carlo simulation with intelligent algorithms such as support vector machine and random survival forest, it breaks the limitations of traditional methods in dealing with high-dimensional heterogeneous data. The entire experimental system, including data cleaning, algorithm adaptation, and parameter optimization, was constructed. Empirical results show that the intelligent model has outstanding performance in extreme market volatility warning and credit default prediction scenarios. Especially in market stress tests, the gap risk capture accuracy of the model is increased by 40% compared with the traditional method, the default identification accuracy of the credit rating model is 89%, and the operational risk warning time is reduced by 60%. The results provide technical support to financial institutions in implementing dynamic risk control systems and encourage the transformation of the risk management paradigm from static assessment to intelligent early warning.