Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market
Hui Ding and
International Review of Economics & Finance, 2021, vol. 72, issue C, 672-689
This study explores the predictive ability of three decomposed realized measures for the US stock market using the mixed data sampling (MIDAS) framework. From the in-sample analysis, we find that all the decomposed realized measures have a significant positive impact on future stock volatility. Moreover, the predictive model, including moderate and extreme volatility, outperforms the related competing models via out-of-sample analysis. We also investigate the predictive sources of moderate and extreme volatility by considering sub-sample and high and low volatility level, and find that the main ability of them is reflected in the low fluctuation period. Furthermore, using a portfolio exercise, we show that the decompositions of moderate and extreme volatility can substantially increase the economic value. Finally, we extend our empirical analysis considering different forecast horizons and non-linear model with regime-switching.
Keywords: Decomposed realized measures; Volatility forecasting; MIDAS model; The US stock market (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:72:y:2021:i:c:p:672-689
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