Community Analysis of Global Financial Markets
We analyze the daily returns of stock market indices and currencies of 56 countries over the period of 2002–2012. We build a network model consisting of two layers, one being the stock market indices and the other the foreign exchange markets. Synchronous and lagged correlations are used as measures...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-05-01
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| Series: | Risks |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2227-9091/4/2/13 |
| _version_ | 1831799159631380480 |
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| author | Irena Vodenska Alexander P. Becker Di Zhou Dror Y. Kenett H. Eugene Stanley Shlomo Havlin |
| author_facet | Irena Vodenska Alexander P. Becker Di Zhou Dror Y. Kenett H. Eugene Stanley Shlomo Havlin |
| author_sort | Irena Vodenska |
| collection | DOAJ |
| description | We analyze the daily returns of stock market indices and currencies of 56 countries over the period of 2002–2012. We build a network model consisting of two layers, one being the stock market indices and the other the foreign exchange markets. Synchronous and lagged correlations are used as measures of connectivity and causality among different parts of the global economic system for two different time intervals: non-crisis (2002–2006) and crisis (2007–2012) periods. We study community formations within the network to understand the influences and vulnerabilities of specific countries or groups of countries. We observe different behavior of the cross correlations and communities for crisis vs. non-crisis periods. For example, the overall correlation of stock markets increases during crisis while the overall correlation in the foreign exchange market and the correlation between stock and foreign exchange markets decrease, which leads to different community structures. We observe that the euro, while being central during the relatively calm period, loses its dominant role during crisis. Furthermore we discover that the troubled Eurozone countries, Portugal, Italy, Greece and Spain, form their own cluster during the crisis period. |
| first_indexed | 2024-12-22T17:40:16Z |
| format | Article |
| id | doaj.art-6c972ba5c44c4dbe93e81289bfbde5df |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-12-22T17:40:16Z |
| publishDate | 2016-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-6c972ba5c44c4dbe93e81289bfbde5df2022-12-21T18:18:25ZengMDPI AGRisks2227-90912016-05-01421310.3390/risks4020013risks4020013Community Analysis of Global Financial MarketsIrena Vodenska0Alexander P. Becker1Di Zhou2Dror Y. Kenett3H. Eugene Stanley4Shlomo Havlin5Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USACenter for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USACenter for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USACenter for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USACenter for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USADepartment of Physics, Bar-Ilan University, Ramat-Gan 52900, IsraelWe analyze the daily returns of stock market indices and currencies of 56 countries over the period of 2002–2012. We build a network model consisting of two layers, one being the stock market indices and the other the foreign exchange markets. Synchronous and lagged correlations are used as measures of connectivity and causality among different parts of the global economic system for two different time intervals: non-crisis (2002–2006) and crisis (2007–2012) periods. We study community formations within the network to understand the influences and vulnerabilities of specific countries or groups of countries. We observe different behavior of the cross correlations and communities for crisis vs. non-crisis periods. For example, the overall correlation of stock markets increases during crisis while the overall correlation in the foreign exchange market and the correlation between stock and foreign exchange markets decrease, which leads to different community structures. We observe that the euro, while being central during the relatively calm period, loses its dominant role during crisis. Furthermore we discover that the troubled Eurozone countries, Portugal, Italy, Greece and Spain, form their own cluster during the crisis period.http://www.mdpi.com/2227-9091/4/2/13community structurecomplex networksfinancial markets |
| spellingShingle | Irena Vodenska Alexander P. Becker Di Zhou Dror Y. Kenett H. Eugene Stanley Shlomo Havlin Community Analysis of Global Financial Markets Risks community structure complex networks financial markets |
| title | Community Analysis of Global Financial Markets |
| title_full | Community Analysis of Global Financial Markets |
| title_fullStr | Community Analysis of Global Financial Markets |
| title_full_unstemmed | Community Analysis of Global Financial Markets |
| title_short | Community Analysis of Global Financial Markets |
| title_sort | community analysis of global financial markets |
| topic | community structure complex networks financial markets |
| url | http://www.mdpi.com/2227-9091/4/2/13 |
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