Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

By Tong Fang, Tae-Hwy Lee, Zhi Su. Journal of Empirical Finance, 2020

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Abstract We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/bc17e482ba2ed894d655ef1e39ec6fd9333130da