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Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market

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  • Lu, Botao
  • Ma, Feng
  • Wang, Jiqian
  • Ding, Hui
  • Wahab, M.I.M.

Abstract

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.

Suggested Citation

  • Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.
    • Handle: RePEc:eee:reveco:v:72:y:2021:i:c:p:672-689
    DOI: 10.1016/j.iref.2020.12.023
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