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

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.