Title: When MIDAS meets LASSO: Forecasting tail risk using effective macroeconomic variables
Authors: Xiaohan Xue - University of East Anglia (United Kingdom) [presenting]
Marwan Izzeldin - Lancaster University (United Kingdom)
Yi Luo - Lancaster University (United Kingdom)
Abstract: A new framework for the joint estimation and forecasting of Value at Risk (VaR) and Expected Shortfall (ES) is proposed, which incorporates low-frequency macroeconomic and financial indicators into the quantile-based MIDAS model. Using an innovative machine-learning approach that maximizes the penalized Asymmetric Laplace (AL) likelihood function with an Adaptive-Lasso penalty, the most informative variables are selected in a ``big data'' setting. A dynamic selection process enables the visualizing of the variable-selection evolution. In the empirical analysis, three variables (namely, realized volatility, term spread and housing starts) are consistently selected for most of the rolling windows and serve as the strongest predictors of future tail risk. More information may be required to predict more extreme VaR and ES. The out-of-sample backtesting results show that our method passes most backtests with relatively higher p-values and achieves the minimum loss in the joint forecasting of VaR and ES.