Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?

Lu Wang, Jiangbin Wu, Yang Cao and Yanran Hong

Energy Economics, 2022, vol. 111, issue C

Abstract: Based on the previous studies that Markov-type GARCH models exhibit inconsistent predictive ability over different horizons, we conduct the improvement of predictive power of renewable energy stock volatility by developing Markov switching GARCH-MIDAS models both in short- and long-terms. By using various out-of-sample tests, the models allowing for regime-switching in the short- and long-volatility components simultaneously outperform other competing models for short-term forecasting. However, the empirical results show that the long-term Markov regime-switching plays a more significant role on the predictive accuracy at longer horizon. Our novel findings indicate that it is necessary to adopt the appropriate predictive models that include short-term, long-term, or both of the above terms in regime-switching. Meanwhile, our extended models indeed provide a more detailed picture of the dynamic behavior over time in renewable energy stock market. Finally, our findings reveals that the governments should adopt a combination of short- and long-term policies when considering the different role of regime shift over different horizons on volatility prediction of the renewable energy stock.

Keywords: Renewable energy stock volatility; GARCH-MIDAS; Markov regime-switching; Short-term forecasting; Long-term forecasting (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:111:y:2022:i:c:s0140988322002237

DOI: 10.1016/j.eneco.2022.106056

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