Dynamics of the Global Stock Market Networks Generated by DCCA Methodology
Abstract
Featured Application
Abstract
1. Introduction
2. Data
3. Methodology
3.1. Detrended cross Correlation Analysis (DCCA)
3.2. Complex Network Analysis and Girvan-Newman Method
3.2.1. Degree
3.2.2. Characteristic Path Length
3.2.3. Efficiency
3.2.4. Clustering Coefficient
3.2.5. Betweenness Centrality
3.2.6. Modularity
3.2.7. Girvan-Newman Method
3.2.8. Create Network Structure
4. Numerical Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Data | Year | |||||||
---|---|---|---|---|---|---|---|---|
Return | 1997 | 3.333 | 1.904 | 0.246 | 0.557 | 0.790 | USA1 | 0.109 |
1998 | 1.583 | 1.825 | 0.099 | 0.451 | 0.880 | USA2 | 0.067 | |
1999 | 0.750 | 2.172 | 0.061 | 0.090 | 0.433 | USA2 | 0.055 | |
2000 | 1.083 | 2.696 | 0.102 | 0.093 | 0.290 | Netherland | 0.121 | |
2001 | 4.583 | 1.445 | 0.263 | 0.488 | 0.819 | USA4 | 0.050 | |
2002 | 3.000 | 1.052 | 0.134 | 0.366 | 0.952 | Netherland | 0.001 | |
2003 | 1.666 | 1.400 | 0.089 | 0.304 | 0.825 | USA1 | 0.016 | |
2004 | 1.833 | 1.538 | 0.108 | 0.298 | 0.636 | USA2 | 0.022 | |
2005 | 1.250 | 1.166 | 0.059 | 0.312 | 0.937 | Netherland | 0.012 | |
2006 | 2.416 | 1.821 | 0.175 | 0.320 | 0.559 | USA2 | 0.090 | |
2007 | 7.000 | 1.766 | 0.437 | 0.726 | 0.836 | Netherland | 0.109 | |
2008 | 8.583 | 1.658 | 0.590 | 0.768 | 0.766 | Japan | 0.128 | |
2009 | 6.333 | 1.790 | 0.461 | 0.613 | 0.893 | Hong Kong | 0.356 | |
2010 | 6.750 | 2.012 | 0.519 | 0.662 | 0.708 | Korea1 | 0.192 | |
2011 | 5.166 | 2.384 | 0.395 | 0.557 | 0.678 | UK | 0.149 | |
2012 | 2.833 | 1.418 | 0.160 | 0.407 | 0.804 | USA2 | 0.060 | |
2013 | 1.500 | 1.530 | 0.088 | 0.297 | 0.766 | UK | 0.047 | |
2014 | 2.083 | 1.324 | 0.112 | 0.357 | 0.904 | USA1 | 0.024 | |
2015 | 2.666 | 1.657 | 0.179 | 0.396 | 0.706 | USA3 | 0.057 | |
2016 | 1.333 | 2.500 | 0.109 | 0.260 | 0.667 | Netherland | 0.095 | |
Volatility | 1997 | 5.750 | 1.633 | 0.327 | 0.605 | 0.859 | France | 0.103 |
1998 | 0.666 | 1.384 | 0.038 | 0.097 | 0.583 | USA2 | 0.012 | |
1999 | 0.166 | 1.000 | 0.007 | 0.000 | 0.000 | - | - | |
2000 | 0.333 | 1.200 | 0.016 | 0.000 | 0.000 | Netherland | 0.004 | |
2001 | 2.000 | 2.620 | 0.152 | 0.353 | 0.663 | USA1 | 0.174 | |
2002 | 1.250 | 1.166 | 0.059 | 0.347 | 0.851 | USA1 | 0.004 | |
2003 | 0.667 | 1.200 | 0.032 | 0.222 | 0.888 | France | 0.008 | |
2004 | 0.750 | 1.357 | 0.041 | 0.097 | 0.583 | USA2 | 0.012 | |
2005 | 0.750 | 1.100 | 0.034 | 0.277 | 0.904 | Netherland | 0.002 | |
2006 | 0.833 | 1.000 | 0.036 | 0.291 | 1.000 | - | - | |
2007 | 2.167 | 3.441 | 0.182 | 0.457 | 0.617 | Argentina | 0.217 | |
2008 | 7.500 | 1.874 | 0.547 | 0.759 | 0.770 | Indonesia | 0.177 | |
2009 | 1.167 | 1.333 | 0.063 | 0.243 | 0.687 | USA2 | 0.020 | |
2010 | 3.083 | 1.990 | 0.238 | 0.481 | 0.711 | UK | 0.162 | |
2011 | 2.416 | 1.469 | 0.139 | 0.363 | 0.749 | USA2 | 0.033 | |
2012 | 0.833 | 1.000 | 0.036 | 0.291 | 1.000 | - | - | |
2013 | 0.667 | 1.384 | 0.038 | 0.097 | 0.583 | USA2 | 0.012 | |
2014 | 1.083 | 1.000 | 0.047 | 0.333 | 1.000 | - | - | |
2015 | 3.333 | 2.219 | 0.233 | 0.454 | 0.676 | Korea2 | 0.095 | |
2016 | 1.083 | 1.862 | 0.070 | 0.281 | 0.812 | USA2 | 0.049 |
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No | Nation | Stock Market | No | Nation | Stock Market |
---|---|---|---|---|---|
1 | Argentina | MERVALS | 13 | Korea1 | KOSPI |
2 | Australia1 | AORD | 14 | Korea2 | KOSDAQ |
3 | Australia2 | S&P ASX200 | 15 | Mexico | IPC |
4 | Brazil | IBOVESPA | 16 | Netherland | AEX |
5 | Chile | IGPA | 17 | Pakistan | KSE100 |
6 | France | CAC40 | 18 | Singapore | STI |
7 | Germany | DAX | 19 | Taiwan | TAIMEX |
8 | Hong Kong | HANGSENG | 20 | USA1 | DOW |
9 | Hungary | BUX | 21 | USA2 | S&P500 |
10 | India | BSE SENSEX | 22 | USA3 | NASDAQ |
11 | Indonesia | IDX | 23 | USA4 | DOW TRANS |
12 | Japan | NIKKEI225 | 24 | UK | FTSE100 |
Figure No. | Community | Stock Market |
---|---|---|
Figure 5a Return Network (1997–2000) | (G1) Europe | France, Germany, Netherland, UK |
(G2) USA | USA1, USA2, USA3, USA4 | |
(G3) Oceania | Australia1, Australia2 | |
(G4) C./S. America | Argentina, Brazil, Mexico | |
Figure 5b Volatility Network (1997–2000) | (G1) Europe | France, Germany, Netherland |
(G2) USA | USA1, USA2, USA4 | |
(G3) Oceania | Australia1, Australia2 | |
(G4) C./S. America | Argentina, Brazil, Mexico | |
Figure 5c Return Network (2001–2004) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3, USA4 | |
Oceania | Australia1, Australia2 | |
(G2) Asia | Korea1, Korea2, Taiwan | |
Figure 5d | (G1) Europe | France, Germany, Netherland, UK |
Volatility Network | USA | USA1, USA2, USA3, USA4 |
(2001-2004) | (G2) Oceania | Australia1, Australia2 |
Figure 5e Return Network (2005–2008) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3 | |
Oceania | Australia1, Australia2 | |
C./S. America | Brazil, Mexico | |
Asia | Hong Kong, India, Japan | |
Figure 5f Volatility Network (2005–2008) | (G1) Europe | France, Germany, Hungary, UK |
USA | USA1, USA2, USA3 | |
C./S. America | Argentina, Brazil, Mexico | |
Asia | Hong Kong, Korea1 | |
Figure 5g Return Network (2009–2012) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3, USA4 | |
Oceania | Australia1, Australia2 | |
C./S. America | Brazil, Mexico | |
Asia | Korea1 | |
Figure 5h Volatility Network (2009–2012) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3, USA4 | |
C./S. America | Brazil, Mexico | |
(G2) Asia | Hong Kong, Indonesia, Taiwan | |
Figure 5i Return Network (2013–2016) | (G1) Europe | France, Germany, Netherland, UK |
(G2) USA | USA1, USA2, USA3, USA4 | |
(G3) Oceania | Australia1, Australia2 | |
(G4) Asia | Hong Kong, Korea1 | |
Figure 5j Volatility Network (2013–2016) | (G1) Europe | France, Germany, Netherland, UK |
(G2) USA | USA1, USA2, USA3, USA4 | |
(G3) Oceania | Australia1, Australia2 | |
(G4) Asia | Korea1, Taiwan | |
Figure 6a Return Network (1997–2006) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3 | |
(G2) C./S. America | Brazil, Mexico | |
(G3) Asia | Korea1, Korea2 | |
Figure 6b | (G1) Europe | France, Germany, Netherland, UK |
Volatility Network | USA | USA1, USA2, USA4 |
(1997–2006) | (G2) Oceania | Australia1, Australia2 |
Figure 6c Return Network (2007–2016) | (G1) Europe | France, Germany, Hungary, Netherland, UK |
USA | USA1, USA2, USA3, USA4 | |
Oceania | Australia1, Australia2 | |
C. America | Mexico | |
Asia | Hong Kong, Japan, Korea1, Taiwan | |
Figure 6d Volatility Network (2007–2016) | (G1) Europe | France, Germany, Hungary, Netherland, UK |
USA | USA1, USA2, USA3, USA4 | |
Oceania | Australia1, Australia2 | |
C./S. America | Argentina, Brazil, Chile, Mexico | |
Asia | Hong Kong, India, Indonesia, Japan, Korea1, Korea2, Taiwan | |
Figure 7a Return Network (1997–2016) | (G1) Europe | France, Germany, Netherland, UK |
USA | USA1, USA2, USA3 | |
Oceania | Australia1, Australia2 | |
(G2) Asia | Korea1, Korea2 | |
Figure 7b | (G1) Europe | France, Germany, Netherland, UK |
Volatility Network | USA | USA1, USA2, USA4 |
(1997–2016) | Oceania | Australia1, Australia2 |
Data | Period | Year | |||||||
---|---|---|---|---|---|---|---|---|---|
return | 4 years | 1997-2000 | 1.250 | 1.812 | 0.079 | 0.361 | 0.866 | USA2 | 0.055 |
2001-2004 | 3.416 | 1.327 | 0.178 | 0.385 | 0.794 | USA3 | 0.036 | ||
2005-2008 | 5.916 | 1.888 | 0.411 | 0.558 | 0.728 | Korea1 | 0.130 | ||
2009-2012 | 6.750 | 1.726 | 0.473 | 0.620 | 0.787 | Hong Kong | 0.157 | ||
2013-2016 | 1.416 | 1.566 | 0.082 | 0.295 | 0.791 | Netherland | 0.024 | ||
10 years | 1997-2006 | 2.083 | 1.553 | 0.128 | 0.327 | 0.791 | UK | 0.035 | |
2007-2016 | 10.500 | 1.447 | 0.602 | 0.805 | 0.842 | Korea1 | 0.069 | ||
20 years | 1997-2016 | 4.833 | 1.737 | 0.341 | 0.508 | 0.785 | Hong Kong | 0.123 | |
volatility | 4 years | 1997-2000 | 0.833 | 1.230 | 0.041 | 0.222 | 0.761 | France | 0.008 |
2001-2004 | 1.583 | 1.379 | 0.086 | 0.300 | 0.808 | USA1 | 0.013 | ||
2005-2008 | 11.083 | 1.380 | 0.619 | 0.814 | 0.862 | Netherland | 0.063 | ||
2009-2012 | 5.000 | 1.921 | 0.360 | 0.541 | 0.698 | Korea1 | 0.165 | ||
2013-2016 | 1.833 | 1.578 | 0.105 | 0.341 | 0.822 | USA1 | 0.024 | ||
10 years | 1997-2006 | 1.667 | 1.310 | 0.088 | 0.268 | 0.881 | USA2 | 0.028 | |
2007-2016 | 17.083 | 1.112 | 0.789 | 0.880 | 0.921 | Netherland | 0.008 | ||
20 years | 1997-2016 | 6.833 | 1.818 | 0.542 | 0.661 | 0.752 | Hong Kong | 0.260 |
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Shin, K.-H.; Lim, G.; Min, S. Dynamics of the Global Stock Market Networks Generated by DCCA Methodology. Appl. Sci. 2020, 10, 2171. https://doi.org/10.3390/app10062171
Shin K-H, Lim G, Min S. Dynamics of the Global Stock Market Networks Generated by DCCA Methodology. Applied Sciences. 2020; 10(6):2171. https://doi.org/10.3390/app10062171
Chicago/Turabian StyleShin, Ki-Hong, Gyuchang Lim, and Seungsik Min. 2020. "Dynamics of the Global Stock Market Networks Generated by DCCA Methodology" Applied Sciences 10, no. 6: 2171. https://doi.org/10.3390/app10062171
APA StyleShin, K.-H., Lim, G., & Min, S. (2020). Dynamics of the Global Stock Market Networks Generated by DCCA Methodology. Applied Sciences, 10(6), 2171. https://doi.org/10.3390/app10062171