TY - JOUR
T1 - Community analysis of global financial markets
AU - Vodenska, Irena
AU - Becker, Alexander P.
AU - Zhou, Di
AU - Kenett, Dror Y.
AU - Stanley, H. Eugene
AU - Havlin, Shlomo
N1 - Publisher Copyright:
© 2016 by the authors; licensee MDPI, Basel, Switzerland.
PY - 2016/6
Y1 - 2016/6
N2 - 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.
AB - 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.
KW - Community structure
KW - Complex networks
KW - Financial markets
UR - http://www.scopus.com/inward/record.url?scp=85007512108&partnerID=8YFLogxK
U2 - 10.3390/risks4020013
DO - 10.3390/risks4020013
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AN - SCOPUS:85007512108
SN - 2227-9091
VL - 4
JO - Risks
JF - Risks
IS - 2
M1 - 13
ER -