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

Paper: arXiv 2412.06193

Authors: Zong Ke, Yuchen Yin

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.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper adapts the MVMQ-CAViaR framework to study tail risk spillovers across US financial markets using quantile regression and optimization algorithms. While the empirical setup covers multiple asset classes over a ten-year period, the paper suffers from readability issues, missing text in sections, and a lack of open code or dataset links for full replication.

Research Flowchart

  flowchart TD
    A[Goal: Predict Tail Risk & Model Spillover Across US Markets] --> B[Data Inputs: US Stocks, Forex, & Credit Bond Markets]
    B --> C[Construct Multivariate Multilevel CAViaR Model]
    C --> D[Optimize Model via Gradient Descent & Genetic Algorithm]
    D --> E[Analyze Spillover Direction, Magnitude & Pseudo-Impulse Response]
    E --> F[Key Outcome: Credit Market Has Highest Risk Spillover & Serves as Primary VaR Predictor]