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Forecasting stock market volatility: The sum of the parts is more than the whole

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  • Gao, Shang
  • Zhang, Zhikai
  • Wang, Yudong
  • Zhang, Yaojie

Abstract

The volatility of financial assets can be decomposed into upside volatility and downside volatility. However, these two components have unique properties, so their predictability is completely different. In this paper, we explore a new forecasting method to predict the S&P 500 volatility by separately modeling upside volatility and downside volatility and summing the forecasts up. Our new method is proved to have better performance compared with directly modeling aggregate volatility. Moreover, the gains in forecast accuracy are robust concerning the individual and combined models.

Suggested Citation

  • Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, vol. 55(PA).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323002210
    DOI: 10.1016/j.frl.2023.103849
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    Cited by:

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    More about this item

    Keywords

    Realized semi-variance; Variance decomposition; HAR model; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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