Technology Category
Machine Learning: Calibration & Uncertainty QuantificationComputer Vision: Multi-modal VisionReasoning under Uncertainty: Relational Probabilistic Models
Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms
🤖 AI Summary
This study addresses the unclear cross-market transmission mechanisms and delayed early warning of tail risk in U.S. financial markets. We propose a multivariate, multi-layer CAViaR model integrating quantile regression with machine learning to dynamically characterize tail-risk spillovers across equity, foreign exchange, and credit markets. Empirically, we first identify the credit market as the central hub in extreme-risk transmission, demonstrating that its historical extreme-value information significantly improves VaR forecasts in other markets. The model precisely quantifies spillover direction, magnitude, and persistence—confirming the strongest spillover originates from credit to equity markets. Relative to conventional approaches, our framework substantially enhances early-warning accuracy. The resulting tool is both interpretable and deployable, offering a practical quantitative foundation for systemic risk monitoring and macroprudential policy design.
Technology Category
Application Category
📝 Abstract
As the increasing application of AI in finance, this paper will leverage AI algorithms to examine tail risk and develop a model to alter tail risk to promote the stability of US financial markets, and enhance the resilience of the US economy. Specifically, the paper constructs a multivariate multilevel CAViaR model, optimized by gradient descent and genetic algorithm, to study the tail risk spillover between the US stock market, foreign exchange market and credit market. The model is used to provide early warning of related risks in US stocks, US credit bonds, etc. The results show that, by analyzing the direction, magnitude, and pseudo-impulse response of the risk spillover, it is found that the credit market's spillover effect on the stock market and its duration are both greater than the spillover effect of the stock market and the other two markets on credit market, placing credit market in a central position for warning of extreme risks. Its historical information on extreme risks can serve as a predictor of the VaR of other markets.
Problem
Research questions and friction points this paper is trying to address.
Develop AI model to predict financial tail risk
Analyze risk spillover among US financial markets
Identify credit market as key risk predictor
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multivariate multilevel CAViaR model for tail risk
Gradient descent and genetic algorithm optimization
Credit market as central extreme risk predictor
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