TY - JOUR
T1 - When MIDAS Meets LASSO
T2 - The Power of Low-Frequency Variables in Forecasting Value-at-Risk and Expected Shortfall
AU - Luo, Yi
AU - Xue, Xiaohan
AU - Izzeldin, Marwan
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - expected shortfall
KW - machine learning
KW - mixed frequency
KW - value-at-risk
UR - http://www.scopus.com/inward/record.url?scp=85216649668&partnerID=8YFLogxK
U2 - 10.1093/jjfinec/nbae016
DO - 10.1093/jjfinec/nbae016
M3 - Article
AN - SCOPUS:85216649668
SN - 1479-8409
VL - 23
JO - Journal of Financial Econometrics
JF - Journal of Financial Econometrics
IS - 1
M1 - nbae016
ER -