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

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  • 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.

Suggested Citation

  • Zong Ke & Yuchen Yin, 2024. "Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms," Papers 2412.06193, arXiv.org.
    • Handle: RePEc:arx:papers:2412.06193
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    File URL: http://arxiv.org/pdf/2412.06193
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    4. Li, Kelong & Xie, Chi & Ouyang, Yingbo & Mo, Tingcheng & Feng, Yusen, 2024. "Tail risk spillovers in the stock and forex markets at the major emergencies: Evidence from the G20 countries," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    5. Kal, Süleyman Hilmi & Arslaner, Ferhat & Arslaner, Nuran, 2015. "The dynamic relationship between stock, bond and foreign exchange markets," Economic Systems, Elsevier, vol. 39(4), pages 592-607.
    6. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
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    Cited by:

    1. Shicheng Zhou & Zizhou Zhang & Rong Zhang & Yuchen Yin & Chia Hong Chang & Qinyan Shen, 2025. "Regression and Forecasting of U.S. Stock Returns Based on LSTM," Papers 2502.05210, arXiv.org, revised May 2025.

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