<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Tang, Yong</dc:creator>
  <dc:creator>Xiong, Jason Jie</dc:creator>
  <dc:creator>Luo, Yong</dc:creator>
  <dc:creator>Zhang, Yi-Cheng</dc:creator>
  <dc:date>2019-01-02</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">The recent financial network analysis approach reveals that the topologies of financial  markets have an important influence on market dynamics. However, the majority of  existing Finance Big Data networks are built as undirected networks without  information on the influence directions among prices. Rather than understanding the  correlations, this research applies the Granger causality test to build the Granger  Causality Directed Network for 33 global major stock market indices. The paper  further analyzes how the markets influence one another by investigating the directed  edges in the different filtered networks. The network topology that evolves in different  market periods is analyzed via a sliding window approach and Finance Big Data  visualization. By quantifying the influences of market indices, 33 global major stock  markets from the Granger causality network are ranked in comparison with the result  based on PageRank centrality algorithm. Results reveal that the ranking lists are  similar in both approaches where the U.S. indices dominate the top position followed  by other American, European, and Asian indices. The lead-lag analysis reveals that  there is lag effects among the global indices. The result sheds new insights on the  influences among global stock markets with implications for trading strategy design,  global portfolio management, risk management, and markets regulation.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/307720</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307720/files/zha_hdg.pdf</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307720/files/zha_hdg_sm.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1080/10864415.2018.1512283</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>International Journal of Electronic Commerce. - 2019, vol. 23, no. 1, p. 85–109</dc:source>
  <dc:subject>info:eu-repo/classification/udc/33</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">How do the global stock markets influence one another? Evidence from finance big data and Granger causality directed network</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
