Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms

TL;DR

This paper develops a multivariate CAViaR model optimized with AI algorithms to analyze and predict tail risk spillovers among US financial markets, aiming to improve early warning systems and market stability.

Contribution

It introduces a novel multilevel CAViaR model optimized by gradient descent and genetic algorithms for tail risk analysis and spillover detection in multiple US markets.

Findings

Credit market has a stronger and longer-lasting spillover effect on stocks.

Historical extreme risk data can predict VaR in other markets.

The model provides early warning signals for financial stability.

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.