Cross-market information transmission and stock market volatility prediction

Yide Wang, Zan Chen and Xiaodong Ji

The North American Journal of Economics and Finance, 2023, vol. 68, issue C

Abstract: The deep integration of global economic and financial activities accelerates the information transmission across financial markets. The cross-market information has become a crucial factor to influence the stock market fluctuation. This paper investigates the explanatory power and impacting mechanism of cross-market information flow in the prediction of Chinese stock market volatility. The empirical results show that the cross-market information flow exhibits significant linear and nonlinear influences on Chinese stock market volatility, and it also appears term-heterogeneous on the prediction accuracy, i.e., the cross-market information flow significantly contributes to medium-term and long-term prediction of Chinese stock market volatility, and the improvement of prediction accuracy is mainly due to nonlinear mechanism, whereas the cross-market information flow performs less value for short-term volatility prediction.

Keywords: Cross-market information flow; Realized volatility; Transfer entropy; Machine learning; Linear and nonlinear Granger causality test (search for similar items in EconPapers)
JEL-codes: C52 C58 G15 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 (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823001006

DOI: 10.1016/j.najef.2023.101977

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