Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

Lu Wang, Feng Ma, Jing Liu and Lin Yang

International Journal of Forecasting, 2020, vol. 36, issue 2, 684-694

Abstract: This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.

Keywords: Stock market; GARCH-MIDAS; Out-of-sample forecasts; Volatility forecasting; Forecasting evaluation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (53)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:684-694

DOI: 10.1016/j.ijforecast.2019.08.005

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