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

Tong Fang, Tae Hwy Lee and Zhi Su

Journal of Empirical Finance, 2020, vol. 58, issue C, 36-49

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.

Keywords: Stock market volatility; GARCH-MIDAS model; Variable selection; Penalized maximum likelihood; Adaptive-Lasso (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 G12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (46)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:58:y:2020:i:c:p:36-49

DOI: 10.1016/j.jempfin.2020.05.007

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