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Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks
We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily...
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Published in: | Computational economics 2018-03, Vol.51 (3), p.607-635 |
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description | We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators. |
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Eugene</creator><creatorcontrib>Wang, Gang-Jin ; Xie, Chi ; Stanley, H. Eugene</creatorcontrib><description>We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.</description><identifier>ISSN: 0927-7099</identifier><identifier>EISSN: 1572-9974</identifier><identifier>DOI: 10.1007/s10614-016-9627-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Behavioral/Experimental Economics ; Clusters ; Computer Appl. in Social and Behavioral Sciences ; Connectors ; Correlation analysis ; Correlation coefficients ; Economic crisis ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Economics and Finance ; Evolution ; Graph theory ; Markets ; Math Applications in Computer Science ; Operations Research/Decision Theory ; Operators ; Securities markets ; Stock exchanges ; Subgroups ; Trees ; Variation</subject><ispartof>Computational economics, 2018-03, Vol.51 (3), p.607-635</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>Computational Economics is a copyright of Springer, (2016). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-485fba37f05676da01e6a06dbddc29bba7bce7081ff5fd3da3e516ea0c0c004a3</citedby><cites>FETCH-LOGICAL-c381t-485fba37f05676da01e6a06dbddc29bba7bce7081ff5fd3da3e516ea0c0c004a3</cites><orcidid>0000-0002-2813-4356</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2007969542/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2007969542?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,12847,27924,27925,33223,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Wang, Gang-Jin</creatorcontrib><creatorcontrib>Xie, Chi</creatorcontrib><creatorcontrib>Stanley, H. Eugene</creatorcontrib><title>Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks</title><title>Computational economics</title><addtitle>Comput Econ</addtitle><description>We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.</description><subject>Behavioral/Experimental Economics</subject><subject>Clusters</subject><subject>Computer Appl. in Social and Behavioral Sciences</subject><subject>Connectors</subject><subject>Correlation analysis</subject><subject>Correlation coefficients</subject><subject>Economic crisis</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Economics</subject><subject>Economics and Finance</subject><subject>Evolution</subject><subject>Graph theory</subject><subject>Markets</subject><subject>Math Applications in Computer Science</subject><subject>Operations Research/Decision Theory</subject><subject>Operators</subject><subject>Securities markets</subject><subject>Stock exchanges</subject><subject>Subgroups</subject><subject>Trees</subject><subject>Variation</subject><issn>0927-7099</issn><issn>1572-9974</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wFvA8-pkP5LGmxa_oGpBxWPIbhLZdrupk10__r1ZK-hF5jAwPO878BByyOCYAYiTwICzPAHGE8lTkYgtMmKFSBMpRb5NRiCHI0i5S_ZCWABAwdJ0RD6mHtE2uqt9Sx867KuuR0t1a-jFm2_677t39NljYyLgqyW91bi0XTiNRG1sW1nq0K_o3GoMkR6yc41drRv6pz0518Eaeme7d4_LsE92nG6CPfjZY_J0efE4vU5m91c307NZUmUT1iX5pHClzoSDggtuNDDLNXBTGlOlsiy1KCsrYMKcK5zJjM5swbjVUMWBXGdjcrTpXaN_7W3o1ML32MaXKo3iJJdFnkaKbagKfQhonVpjvdL4qRioQbDaCFZRsBoEKxEz6SYTItu-WPxt_j_0BdvpgGk</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Wang, Gang-Jin</creator><creator>Xie, Chi</creator><creator>Stanley, H. 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Eugene</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-485fba37f05676da01e6a06dbddc29bba7bce7081ff5fd3da3e516ea0c0c004a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Behavioral/Experimental Economics</topic><topic>Clusters</topic><topic>Computer Appl. in Social and Behavioral Sciences</topic><topic>Connectors</topic><topic>Correlation analysis</topic><topic>Correlation coefficients</topic><topic>Economic crisis</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Economics</topic><topic>Economics and Finance</topic><topic>Evolution</topic><topic>Graph theory</topic><topic>Markets</topic><topic>Math Applications in Computer Science</topic><topic>Operations Research/Decision Theory</topic><topic>Operators</topic><topic>Securities markets</topic><topic>Stock exchanges</topic><topic>Subgroups</topic><topic>Trees</topic><topic>Variation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Gang-Jin</creatorcontrib><creatorcontrib>Xie, Chi</creatorcontrib><creatorcontrib>Stanley, H. 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Eugene</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks</atitle><jtitle>Computational economics</jtitle><stitle>Comput Econ</stitle><date>2018-03-01</date><risdate>2018</risdate><volume>51</volume><issue>3</issue><spage>607</spage><epage>635</epage><pages>607-635</pages><issn>0927-7099</issn><eissn>1572-9974</eissn><abstract>We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10614-016-9627-7</doi><tpages>29</tpages><orcidid>https://orcid.org/0000-0002-2813-4356</orcidid></addata></record> |
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subjects | Behavioral/Experimental Economics Clusters Computer Appl. in Social and Behavioral Sciences Connectors Correlation analysis Correlation coefficients Economic crisis Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Evolution Graph theory Markets Math Applications in Computer Science Operations Research/Decision Theory Operators Securities markets Stock exchanges Subgroups Trees Variation |
title | Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks |
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