Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis

Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis

Abstract Value at risk (VaR) and expected shortfall (ES) have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions, external regulations, and risk capital allocation. However, existing VaR estimation approaches fail to accura...

Full description

Bibliographic Details
Main Authors: Jianzhou Wang, Shuai Wang, Mengzheng Lv, He Jiang
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Financial Innovation
Subjects:
Value at risk
Expected shortfall
Quantile regression
Recurrent neural networks
Generative adversarial networks
Online Access:https://doi.org/10.1186/s40854-023-00564-5

Internet

https://doi.org/10.1186/s40854-023-00564-5

Similar Items