THE TOPOLOGICAL STRUCTURE OF THE GLOBAL FOREIGN EXCHANGE MARKET DURING CRISES – COMPARATIVE NETWORK ANALYSIS
DOI:
https://doi.org/10.15290/Słowa kluczowe:
foreign exchange market, minimum spanning tree, DTW distanceAbstrakt
Purpose – The aim of this article was to assess the changes in the topological structure of the currency market caused by two crises: the COVID-19 pandemic in 2020 and Russia’s aggression against Ukraine in 2022. A network of major world currencies was analysed over three six-month sub-periods: the pandemic period 1.02–31.07.2020, the war period 1.02–31.07.2022 and the reference period 1.02–31.07.2021.
Research method – We have used the dynamic time warping (DTW) method for comparing time series. DTW distances between pairs of individual currencies were calculated, and, based on them, minimum spanning trees (MST) were constructed, whose topological characteristics were analysed.
Results – It turned out that the topological structure of the foreign exchange market varies in the sub-periods studied, and the analysed crises affected the currency network. In addition, the networks generated by the MST depend on the choice of base currency used to measure the value of all other currencies.
Originality / value / implications / recommendations – The significance of the results obtained lies in providing a description of the topological structure of the market during the observed crises. The detected hierarchical structures can be useful in theoretical descriptions of currencies and in the search for economic factors affecting specific groups of countries.
Bibliografia
Bellman R., Kalaba R., 1959, On adaptive control processes, “IRE Transactions on Automatic Control”, vol. 4(2), pp. 1-9, DOI: 10.1109/tac.1959.1104847.
Chakraborty A., Easwaran S., Sinha S., 2020, Uncovering Hierarchical Structure of International FOREX Market by Using Similarity Metric between Fluctuation Distributions of Currencies, “Acta Physica Polonica A”, vol. 138(1), pp. 105-115, DOI: 10.12693/aphyspola.138.105.
Fenn D., 2010, Network Communities and the Foreign Exchange Market, University of Oxford, Oxford.
Górski A., Drożdż S., Kwapień J., Oświęcimka P., 2006, Complexity Characteristics of Currency Networks, “Acta Physica Polonica B”, vol. 37(11), pp. 2987-2995.
Gupta K., Chatterjee N., 2020, Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As COVID-19 Crises Unfolds, arXiv:2004.10560 [q-fin.ST], DOI: 10.48550/arXiv.2004.10560.
Jang W., Lee J., Chang W., 2011, Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree, “Physica A: Statistical Mechanics and its Applications”, vol. 390(4), pp. 707-718, DOI: 10.1016/j.physa.2010.10.028.
Kazemilari M, Mohamadi A., 2018, Topological Network Analysis Based on Dissimilarity Measure of Multivariate Time Series Evolution in the Subprime Crisis, “International Journal of Financial Studies”, vol. 6(2), 47, DOI: 10.3390/ijfs6020047.
Keskin M., Deviren B., Kocakaplan Y., 2011, Topology of the Correlation Networks among Major Currencies using Hierarchical Structure Methods, “Physica A: Statistical Mechanics and its Applications”, vol. 390, pp. 719-730, DOI: 10.1016/j.physa.2010.10.041.
Kruskal J., 1956, On the shortest spanning subtree of a graph and the traveling salesman problem, “Proceedings of the American Mathematical Society”, vol. 7, pp. 48-50, DOI: 10.1090/s0002-9939-1956-0078686-7.
Kwapień J., Gworek S., Drożdż S., 2009, Structure and evolution of the foreign exchange networks, “Acta Physica Polonica B”, vol. 40, pp. 175-194.
Mantegna R., 1999, Hierarchical structure in financial markets, “The European Physical Journal B - Condensed Matter and Complex Systems”, vol. 11(1), pp. 193-197, DOI: 10.1007/s100510050929.
Miśkiewicz J., 2021, Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market, “Entropy”, vol. 23(3), pp. 1-19, DOI: 10.3390/e23030352.
Mizuno T., Takayasu H., Takayasu M., 2006, Correlation networks among currencies, “Physica A: Statistical Mechanics and its Applications”, vol. 364, pp. 336-342, DOI: 10.1016/j.physa.2005.08.079.
Naylor M., Rose L., Moyle B., 2007, Topology of foreign exchange markets using hierarchical structure methods, “Physica A: Statistical Mechanics and its Applications”, vol. 382(1), pp. 199-208, DOI: 10.1016/j.physa.2007.02.019.
Newman M., Girvan M., 2004, Finding and evaluating community structure in networks, “Physical Review E”, vol. 69, 026113, DOI: 10.1103/physreve.67.026126.
Ortega G., Matesanz D., 2006, Cross-country hierarchical structure and currency crises, “International Journal of Modern Physics C”, vol. 17(3), pp. 333-341, DOI: 10.1142/s012918310600856x.
Prim R., 1957, Shortest connection networks and some generalizations, “Bell System Technical Journal”, vol. 36(6), pp. 1389-1401, DOI: 10.1002/j.1538-7305.1957.tb01515.x.
Wang G., Xie C., 2016, Tail dependence structure of the foreign exchange market: A network view, “Expert Systems with Applications”, vol. 46, pp. 164-179, DOI: 10.1016/j.eswa.2015.10.037.
Wang G., Xie C., Han F., Sun B., 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”, vol. 391(16), pp. 4136-4146, DOI: 10.1016/j.physa.2012.03.036.
Wang, G., Xie C., Zhang P., Han F., Chen S., 2014, Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach, “Discrete Dynamics in Nature and Society”, vol. 170921, pp. 1-11, DOI: 10.1155/2014/170921.
Zhang D., Zhuang Y., Tang P., Han Q., 2022, The evolution of foreign exchange market: A network view, “Physica A: Statistical Mechanics and its Applications”, vol. 608(2), 128311, DOI: 10.1016/j.physa.2022.128311.
Pobrania
Opublikowane
Numer
Dział
Licencja
Prawa autorskie (c) 2024 Optimum. Economic StudiesAutor zapoznał się i akceptuje warunki zawarte w umowie licencyjnej
