Analyzing the dynamic sectoral influence in Chinese and American stock markets

Hu Tian, Xiaolong Zheng, Dajun Zeng

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

In this paper, we mainly focus on examining the sectoral influence on fine time scales in the Chinese and American stock markets. Based on the dataset regarding the 10 sector indices, we construct the sectoral-level causal networks by incorporating the empirical mode decomposition into Granger causal test and find that the most influential sectors on different time scales are almost different except that industrial sector has prominent influence on all time scales in the Chinese stock markets. We further confirm that the influence of dominant sectors on different time scale is stable both in the Chinese and American stock markets. Especially, we investigate the periods of some extreme market events such as the 2008 financial crisis, and obtain that the stock market collapse and soar events can improve the statistical causality of sectors and enhances the linkages among sectors on the long time scale. These findings can provide significant insights for policymakers and investors to understand the underlying differences regarding the dynamic sectoral influence in stock markets of developing and developed countries.

Original languageEnglish (US)
Article number120922
JournalPhysica A: Statistical Mechanics and its Applications
DOIs
StatePublished - Jan 1 2019

Keywords

  • Causal network
  • Empirical mode decomposition
  • Granger causality
  • Multi-time scales
  • Sectoral influence

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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