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Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree

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  • Wang, Gang-Jin
  • Xie, Chi
  • Han, Feng
  • Sun, Bo

Abstract

In this study, we employ a dynamic time warping method to study the topology of similarity networks among 35 major currencies in international foreign exchange (FX) markets, measured by the minimal spanning tree (MST) approach, which is expected to overcome the synchronous restriction of the Pearson correlation coefficient. In the empirical process, firstly, we subdivide the analysis period from June 2005 to May 2011 into three sub-periods: before, during, and after the US sub-prime crisis. Secondly, we choose NZD (New Zealand dollar) as the numeraire and then, analyze the topology evolution of FX markets in terms of the structure changes of MSTs during the above periods. We also present the hierarchical tree associated with the MST to study the currency clusters in each sub-period. Our results confirm that USD and EUR are the predominant world currencies. But USD gradually loses the most central position while EUR acts as a stable center in the MST passing through the crisis. Furthermore, an interesting finding is that, after the crisis, SGD (Singapore dollar) becomes a new center currency for the network.

Suggested Citation

  • Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "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, Elsevier, vol. 391(16), pages 4136-4146.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:16:p:4136-4146 DOI: 10.1016/j.physa.2012.03.036
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Hailin, 2015. "Piecewise aggregate representations and lower-bound distance functions for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 10-25.
    2. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Sep 2017.
    3. repec:spr:jeicoo:v:12:y:2017:i:3:d:10.1007_s11403-016-0176-x is not listed on IDEAS
    4. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    5. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    6. repec:eee:phsmap:v:490:y:2018:i:c:p:1335-1343 is not listed on IDEAS
    7. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2013. "Minimal spanning tree problem in stock networks analysis: An efficient algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2226-2234.
    8. Wang, Gang-Jin & Xie, Chi, 2015. "Correlation structure and dynamics of international real estate securities markets: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 176-193.
    9. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    10. Wang, Gang-Jin & Xie, Chi & Chen, Shou & Yang, Jiao-Jiao & Yang, Ming-Yan, 2013. "Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3715-3730.
    11. Sun, Xuelian & Liu, Zixian, 2016. "Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 667-679.
    12. Wang, Gang-Jin & Xie, Chi, 2013. "Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1418-1428.
    13. V. A. Kalyagin & P. A. Koldanov & P. M. Pardalos, 2015. "Optimal decision for the market graph identification problem in sign similarity network," Papers 1512.06449, arXiv.org.
    14. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    15. Mansooreh Kazemilari & Maman Abdurachman Djauhari & Zuhaimy Ismail, 2016. "Foreign Exchange Market Performance: Evidence from Bivariate Time Series Approach," Papers 1608.07694, arXiv.org.

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