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Discovering the drivers of stock market volatility in a data-rich world

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  • Chun, Dohyun
  • Cho, Hoon
  • Ryu, Doojin

Abstract

This study comprehensively examines the economic and financial drivers of volatility changes in terms of a cross-country perspective. We review a wide range of studies related to financial volatility forecasting and collect a diverse set of prediction variables. By analyzing them within a unified framework, we find that only a small number of variables contain significant predictive information. Most of all, we discover that among various global market indicators, Chinese stock market movements significantly predict U.S. stock market volatility. Further analyses provide evidence of the effect of Chinese stock market movements on the U.S. stock market.

Suggested Citation

  • Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:intfin:v:82:y:2023:i:c:s1042443122001561
    DOI: 10.1016/j.intfin.2022.101684
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    More about this item

    Keywords

    Asset allocation; Cross-market studies; Global financial markets; Heterogeneous autoregressive model; LASSO; Volatility forecasting; C53; C55; G17;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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