When MIDAS Meets LASSO: The Power of Low-Frequency Variables in Forecasting Value-at-Risk and Expected Shortfall

Luo, Yi, Xue, Xiaohan and Izzeldin, Marwan (2024) When MIDAS Meets LASSO: The Power of Low-Frequency Variables in Forecasting Value-at-Risk and Expected Shortfall. Journal of Financial Econometrics, 23 (1). ISSN 1479-8409

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Abstract

We propose a new framework for the joint estimation and forecasting of Value-at-Risk (VaR) and Expected Shortfall (ES) that integrates low-frequency variables. By maximizing the Asymmetric Laplace likelihood function with an Adaptive Lasso penalty, the most informative variables are selected on a rolling-window basis. In the empirical analysis, realized volatility, term spread, and housing starts serve as the strongest predictors of future tail risk. The out-of-sample backtesting results demonstrate that our method significantly outperforms other benchmarks, and achieves minimum loss in the joint forecasting of both the one-day-ahead and multi-day-ahead extreme S&P500 VaR and ES.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2024 The Author(s). Published by Oxford University Press. All rights reserved.
Uncontrolled Keywords:	expected shortfall,machine learning,mixed frequency,value-at-risk,finance,economics and econometrics ,/dk/atira/pure/subjectarea/asjc/2000/2003
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	05 Jun 2026 09:42
Last Modified:	05 Jun 2026 09:42
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103287
DOI:	10.1093/jjfinec/nbae016

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