A0169
Title: When MIDAS meets LASSO: The wisdom of low-frequency variables in forecasting value-at-risk and expected shortfall
Authors: Yi Luo - Xian Jiaotong-Liverpool University (China) [presenting]
Xiaohan Xue - University of East Anglia (United Kingdom)
Marwan Izzeldin - Lancaster University Management School (United Kingdom)
Abstract: A new framework is proposed for the joint estimation and forecasting of value-at-risk (VaR) and expected shortfall (ES), integrating low-frequency variables. By maximizing the asymmetric Laplace (AL) 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 show that the method consistently outperforms other benchmark models and achieves the minimum loss in the joint forecasting of VaR and ES.
