Forecasting global stock market volatilities in an uncertain world

Zhao-Chen Li, Chi Xie, Zhi-Jian Zeng, Gang-Jin Wang and Ting Zhang

International Review of Financial Analysis, 2023, vol. 85, issue C

Abstract: We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting; the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias; and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.

Keywords: Global stock markets; Realized volatility; Uncertainty measures; HAR framework; Out-of-sample forecasts (search for similar items in EconPapers)
JEL-codes: C52 C53 C58 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:85:y:2023:i:c:s1057521922004136

DOI: 10.1016/j.irfa.2022.102463

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