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

Author:

Luo Yi1ORCID,Xue Xiaohan2ORCID,Izzeldin Marwan3

Affiliation:

1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University , Suzhou, China

2. Accounting, Finance and Law Division, School of Management, University of Bath , Bath, UK

3. Department of Economics, Lancaster University , Lancaster, UK

Abstract

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.

Publisher

Oxford University Press (OUP)

Reference68 articles.

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