Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection

Tong Fang, Tae Hwy Lee and Zhi Su
Additional contact information
Tong Fang: Shandong University
Zhi Su: Central University of Finance and Economics

No 202009, Working Papers from University of California at Riverside, Department of Economics

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)
Pages: 31 Pages
Date: 2020-05
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk, nep-for and nep-ore
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Citations: View citations in EconPapers (47)

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Journal Article: Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202009

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