Deep Learning-Based CoVaR Forecasting

DOI

10.2991/978-94-6239-699-9_37How to use a DOI?

Keywords

CoVaR Forecasting; Deep Learning; Tail Risk

Abstract

This paper examines the feasibility of deep learning for Conditional Value at Risk forecasting in international stock markets. Using daily data for the stock markets of China, the United States, Japan, Germany, and Brazil from 1 July 2010 to 1 July 2025, the study develops a CNN-Transformer quantile regression model for CoVaR prediction. The empirical analysis is based on log return series, representative forecast plots, and the Diebold-Mariano test against benchmark models. The results show that the predicted CoVaR series are clearly time-varying and become more negative during periods of market stress, indicating that the proposed model captures meaningful dynamics in conditional tail risk. The Diebold-Mariano test further shows that the proposed model outperforms both the CNN-QR benchmark and the Transformer-QR benchmark, while the overall test results remain positive for all market pairs. These findings suggest that combining local feature extraction with long-range dependency modeling helps improve CoVaR forecasting performance. The study provides empirical support for the application of deep learning to CoVaR prediction and contributes to the literature on tail risk forecasting in international stock markets.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dan Yang
PY  - 2026
DA  - 2026/06/02
TI  - Deep Learning-Based CoVaR Forecasting
BT  - Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026)
PB  - Atlantis Press
SP  - 355
EP  - 361
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-699-9_37
DO  - 10.2991/978-94-6239-699-9_37
ID  - Yang2026
ER  -