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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 170921, 11 pages
http://dx.doi.org/10.1155/2014/170921
Research Article

Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach

1College of Business Administration, Hunan University, Changsha 410082, China
2Center of Finance and Investment Management, Hunan University, Changsha 410082, China
3China Merchants Bank, Shenzhen 518067, China

Received 11 March 2014; Accepted 16 April 2014; Published 6 May 2014

Academic Editor: Fenghua Wen

Copyright © 2014 Gang-Jin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. N. Mantegna and H. E. Stanley, Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press, Cambridge, UK, 1999. View at MathSciNet
  2. J. Kwapień and S. Drożdż, “Physical approach to complex systems,” Physics Reports, vol. 515, no. 3-4, pp. 115–226, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  3. C. Huang, C. Peng, X. Chen, and F. Wen, “Dynamics analysis of a class of delayed economic model,” Abstract and Applied Analysis, vol. 2013, Article ID 962738, 12 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  4. C. Huang, H. Kuang, X. Chen, and F. Wen, “An LMI approach for dynamics of switched cellular neural networks with mixed delays,” Abstract and Applied Analysis, vol. 2013, Article ID 870486, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. R. N. Mantegna, “Hierarchical structure in financial markets,” European Physical Journal B, vol. 11, no. 1, pp. 193–197, 1999. View at Google Scholar · View at Scopus
  6. V. Boginski, S. Butenko, and P. M. Pardalos, “Statistical analysis of financial networks,” Computational Statistics and Data Analysis, vol. 48, no. 2, pp. 431–443, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. J.-P. Onnela, K. Kaski, and J. Kertész, “Clustering and information in correlation based financial networks,” European Physical Journal B, vol. 38, no. 2, pp. 353–362, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna, “A tool for filtering information in complex systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 30, pp. 10421–10426, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. J.-P. Onnela, A. Chakraborti, K. Kaski, and J. Kertész, “Dynamic asset trees and portfolio analysis,” European Physical Journal B, vol. 30, no. 3, pp. 285–288, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, and A. Kanto, “Dynamics of market correlations: taxonomy and portfolio analysis,” Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, vol. 68, no. 5, Article ID 056110, 12 pages, 2003. View at Google Scholar · View at Scopus
  11. J. G. Brida and W. A. Risso, “Dynamics and structure of the 30 largest North American companies,” Computational Economics, vol. 35, no. 1, pp. 85–99, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. W.-Q. Huang, X.-T. Zhuang, and S. Yao, “A network analysis of the Chinese stock market,” Physica A: Statistical Mechanics and Its Applications, vol. 388, no. 14, pp. 2956–2964, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. C. K. Tse, J. Liu, and F. C. M. Lau, “A network perspective of the stock market,” Journal of Empirical Finance, vol. 17, no. 4, pp. 659–667, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Y. Kenett, M. Tumminello, A. Madi, G. Gur-Gershgoren, R. N. Mantegna, and E. Ben-Jacob, “Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market,” PLoS ONE, vol. 5, no. 12, Article ID e15032, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Z. Górski, S. Drożdż, and J. Kwapień, “Scale free effects in world currency exchange network,” The European Physical Journal B, vol. 66, no. 1, pp. 91–96, 2008. View at Google Scholar
  16. J. Kwapień, S. Gworek, S. Drożdż, and A. Górski, “Analysis of a network structure of the foreign currency exchange market,” Journal of Economic Interaction and Coordination, vol. 4, no. 1, pp. 55–72, 2009. View at Google Scholar
  17. J. Kwapień, A. Górski, and S. Drożdż, “Structure and evolution of the foreign exchange networks,” Acta Physica Polonica B, vol. 40, no. 1, pp. 175–194, 2009. View at Google Scholar
  18. W. Jang, J. Lee, and W. Chang, “Currency crises and the evolution of foreign exchange market: evidence from minimum spanning tree,” Physica A: Statistical Mechanics and Its Applications, vol. 390, no. 4, pp. 707–718, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. G.-J. Wang, C. Xie, F. Han, and B. Sun, “Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: evidence from minimal spanning tree,” Physica A: Statistical Mechanics and Its Applications, vol. 391, no. 16, pp. 4136–4146, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Matesanz and G. J. Ortega, “Network analysis of exchange data: Interdependence drives crisis contagion,” Quality & Quantity, 2013. View at Publisher · View at Google Scholar
  21. G.-J. Wang, C. Xie, Y.-J. Chen, and S. Chen, “Statistical properties of the foreign exchange network at different time scales: evidence from detrended cross-correlation coefficient and minimum spanning tree,” Entropy, vol. 15, no. 5, pp. 1643–1662, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  22. D.-M. Song, M. Tumminello, W.-X. Zhou, and R. N. Mantegna, “Evolution of worldwide stock markets, correlation structure, and correlation-based graphs,” Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, vol. 84, no. 2, Article ID 026108, 9 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. Š. Lyócsa, T. Výrost, and E. Baumöhl, “Stock market networks: the dynamic conditional correlation approach,” Physica A: Statistical Mechanics and Its Applications, vol. 391, no. 16, pp. 4147–4158, 2012. View at Google Scholar
  24. C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model,” Abstract and Applied Analysis, vol. 2013, Article ID 143194, 13 pages, 2013. View at Publisher · View at Google Scholar
  25. T. Trancoso, “Emerging markets in the global economic network: real(ly) decoupling?” Physica A: Statistical Mechanics and Its Applications, vol. 395, pp. 499–510, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  26. M. Sklar, “Fonctions de répartition à n dimensions et leurs marges,” Publications de l’Institut de Statistique de l’Université de Paris, vol. 8, pp. 229–231, 1959. View at Google Scholar · View at MathSciNet
  27. A. Sklar, “Random variables, joint distribution functions, and copulas,” Kybernetika, vol. 9, pp. 449–460, 1973. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  28. A. C. Cameron, T. Li, P. K. Trivedi, and D. M. Zimmer, “Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts,” The Econometrics Journal, vol. 7, no. 2, pp. 566–584, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  29. F. Wen and Z. Liu, “A copula-based correlation measure and its application in chinese stock market,” International Journal of Information Technology & Decision Making, vol. 8, no. 4, pp. 787–801, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Hu, “Dependence structures in Chinese and US financial markets: a time-varying conditional copula approach,” Applied Financial Economics, vol. 20, no. 7, pp. 561–583, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. A. J. Patton, “Estimation of multivariate models for time series of possibly different lengths,” Journal of Applied Econometrics, vol. 21, no. 2, pp. 147–173, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. R. Aloui, M. S. Ben Aïssa, and D. K. Nguyen, “Conditional dependence structure between oil prices and exchange rates: a copula-GARCH approach,” Journal of International Money and Finance, vol. 32, pp. 719–738, 2013. View at Google Scholar
  33. R. Aloui, S. Hammoudeh, and D. K. Nguyen, “A time-varying copula approach to oil and stock market dependence: the case of transition economies,” Energy Economics, vol. 39, pp. 208–221, 2013. View at Google Scholar
  34. K. Wang, Y.-H. Chen, and S.-W. Huang, “The dynamic dependence between the Chinese market and other international stock markets: a time-varying copula approach,” International Review of Economics and Finance, vol. 20, no. 4, pp. 654–664, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. A. J. Patton, “Modelling asymmetric exchange rate dependence,” International Economic Review, vol. 47, no. 2, pp. 527–556, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. C. Diks, V. Panchenko, and D. van Dijk, “Out-of-sample comparison of copula specifications in multivariate density forecasts,” Journal of Economic Dynamics and Control, vol. 34, no. 9, pp. 1596–1609, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Dias and P. Embrechts, “Modeling exchange rate dependence dynamics at different time horizons,” Journal of International Money and Finance, vol. 29, no. 8, pp. 1687–1705, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Review, vol. 51, no. 4, pp. 661–703, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  39. Y. Wei, Y. Wang, and D. Huang, “A copula-multifractal volatility hedging model for CSI 300 index futures,” Physica A: Statistical Mechanics and Its Applications, vol. 390, no. 23-24, pp. 4260–4272, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Lai, C. W. S. Chen, and R. Gerlach, “Optimal dynamic hedging via copula-threshold-GARCH models,” Mathematics and Computers in Simulation, vol. 79, no. 8, pp. 2609–2624, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. H. Joe and J. J. Xu, “The estimation method of inference functions for margins for multivariate models,” Tech. Rep. 166, Department of Statistics, University of British Columbia, Vancouver, Canada, 1996. View at Google Scholar
  42. J. B. Kruskal Jr., “On the shortest spanning subtree of a graph and the traveling salesman problem,” Proceedings of the American Mathematical Society, vol. 7, pp. 48–50, 1956. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  43. C. Yang, Y. Shen, and B. Xia, “Evolution of Shanghai stock market based on maximal spanning trees,” Modern Physics Letters B, vol. 27, no. 3, Article ID 135002, 19 pages, 2013. View at Google Scholar
  44. N. Vandewalle, F. Brisbois, and X. Tordoir, “Non-random topology of stock markets,” Quantitative Finance, vol. 1, no. 3, pp. 372–374, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  45. R. Albert and A.-L. Barabási, “Statistical mechanics of complex networks,” Reviews of Modern Physics, vol. 74, no. 1, pp. 47–97, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  46. T. Aste, W. Shaw, and T. Di Matteo, “Correlation structure and dynamics in volatile markets,” New Journal of Physics, vol. 12, no. 8, Article ID 085009, 21 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. T. Qiu, B. Zheng, and G. Chen, “Financial networks with static and dynamic thresholds,” New Journal of Physics, vol. 12, no. 4, Article ID 043057, 16 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of DNA nucleotides,” Physical Review E, vol. 49, no. 2, pp. 1685–1689, 1994. View at Publisher · View at Google Scholar · View at Scopus