The COVID-19 pandemic affected the entire global economic system, including currency exchange rates. The main objective of this study is to assess the similarity between time series of currency exchange rates before and during the COVID-19 crisis. In addition, the study aims to examine the relationship between the exchange rates of currencies and the COVID-19 time series in particular countries. The Dynamic Time Warping (DTW) method was applied to check if changes in the exchange rates were related to the spread of COVID-19, and if they were, to what extent it was so. The use of the DTW allows the calculation of the distance between analysed time series. In this study, it made it possible to group the analysed currencies according to their change relative to the pandemic dynamics. The study is based on data from the Stooq and Our World in Data websites. Data on the 17 studied currencies denominated in the New Zealand dollar came from the period between 1 January 2019 and 10 November 2021, and the COVID-19 data from the period between 1 March 2020 and 10 November 2021. The results demonstrate that exchange rates evolved differently in all the three analysed periods: the pre-pandemic period and the first and the second phase of the pandemic. The outbreak of the pandemic led to the concentration of most currencies around the US dollar. However, when the economies unfroze, a polarisation of the currency market occurred, with the world’s major currencies clustering either around the US dollar or the euro.
currency exchange rates, COVID-19 pandemic, Dynamic Time Warping, DTW
C58, C32, C38
Aach, J., & Church, G. M. (2001). Aligning gene expression time series with time warping algorithms. Bioinformatics, 17(6), 495–508. https://doi.org/10.1093/bioinformatics/17.6.495.
Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering – A decade review. Information Systems, 53, 16–38. https://doi.org/10.1016/j.is.2015.04.007.
Arici, T., Celebi, S., Aydin, A. S., & Temiz, T. T. (2014). Robust gesture recognition using feature pre-processing and weighted dynamic time warping. Multimedia Tools and Applications, 72(3), 3045–3062. https://doi.org/10.1007/s11042-013-1591-9.
Bellman, R., & Kalaba, R. (1959). On adaptive control processes. IRE Transactions on Automatic Control, 4(2), 1–9. https://doi.org/10.1109/TAC.1959.1104847.
Cao, H., Guo, Z., Li, Y., & Ran, Z. (2020). The Relationship Structure of Global Exchange Rate Based on Network Analysis. Journal of Mathematical Finance, 10(1), 58–76. https://doi.org/10.4236/jmf.2020.101006.
Devpura, N. (2021). Effect of COVID-19 on the relationship between Euro/USD exchange rate and oil price. MethodsX, 8, 1–8. https://doi.org/10.1016/j.mex.2021.101262.
Feng, G.-F., Yang, H.-C., Gong, Q., & Chang, C.-P. (2021). What is the exchange rate volatility response to COVID-19 and government interventions? Economic Analysis and Policy, 69, 705– 719. https://doi.org/10.1016/j.eap.2021.01.018.
Giorgino, T. (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1–24. https://doi.org/10.18637/jss.v031.i07.
Górski, A. Z., Drożdż, S., Kwapień, J., & Oświęcimka, P. (2008). Minimal Spanning Tree graphs and power like scaling in FOREX networks. Acta Physica Polonica A, 114(3), 531–538. https://doi.org/10.48550/ARXIV.0809.0437.
Gupta, K., & Chatterjee, N. (2020). Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As Covid-19 Crises Unfolds. https://arxiv.org/ftp/arxiv/papers/2004/2004.10560.pdf.
Hong, M. Y., & Yoon, J. W. (2022). The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information. PLoS ONE, 17(2), 1–24. https://doi.org/10.1371/journal.pone.0259869.
Iqbal, N., Fareed, Z., Shahzad, F., He, X., Shahzad, U., & Lina, M. (2020). The nexus between COVID-19, temperature and exchange rate in Wuhan city: New findings from partial and multiple wavelet coherence. Science of The Total Environment, 729, 1–9. https://doi.org/10.1016/j.scitotenv.2020.138916.
Iyke, B. N. (2020). The Disease Outbreak Channel of Exchange Rate Return Predictability: Evidence from COVID-19. Emerging Markets Finance and Trade, 56(10), 2277–2297. https://doi.org/10.1080/1540496X.2020.1784718.
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, 6(2), 1–16. https://doi.org/10.3390/ijfs6020047.
Keogh, E., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3), 358–386. https://doi.org/10.1007/s10115-004-0154-9.
Li, C., Su, Z.-W., Yaqoob, T., & Sajid, Y. (2021). COVID-19 and currency market: a comparative analysis of exchange rate movement in China and USA during pandemic. Economic Research – Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2021.1959368.
Limas, E. (2019). An application of minimal spanning trees and hierarchical trees to the study of Latin American exchange rates. Journal of Dynamics and Games, 6(2), 131–148. https://doi.org/10.3934/jdg.2019010.
Łuczak, M. (2018). Combining raw and normalized data in multivariate time series classification with dynamic time warping. Journal of Intelligent and Fuzzy Systems, 34(1), 373–380. https://doi.org/10.3233/JIFS-171393.
Miśkiewicz, J. (2021). Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market. Entropy, 23(3), 1–19. https://doi.org/10.3390/e23030352.
Müller, M. (2007). Information Retrieval for Music and Motion. Springer-Verlag. https://doi.org/10.1007/978-3-540-74048-3.
Myers, C. S., & Rabiner, L. R. (1981). A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal, 60(7), 1389– 1409. https://doi.org/10.1002/j.1538-7305.1981.tb00272.x.
Narayan, P. K. (2020). Has COVID-19 Changed Exchange Rate Resistance to Shocks?. Asian Economics Letters, 1(1), 1–4. https://doi.org/10.46557/001c.17389.
Narayan, P. K., Devpura, N., & Wang, H. (2020). Japanese currency and stock market – What happened during the COVID-19 pandemic?. Economic Analysis and Policy, 68, 191–198. https://doi.org/10.1016/j.eap.2020.09.014.
Naylor, M. J., Rose, L. C., & Moyle, B. J. (2007). Topology of foreign exchange markets using hierarchical structure methods. Physica A: Statistical Mechanics and its Applications, 382(1), 199–208. https://doi.org/10.1016/j.physa.2007.02.019.
Ortega, G. J., & Matesanz, D. (2006). Cross-country hierarchical structure and currency crises. International Journal of Modern Physics C, 17(3), 333–341. https://doi.org/10.1142/S012918310600856X.
Pasiouras, A., & Daglis, T. (2020). The dollar exchange rates in the Covid-19 era: Evidence from 5 currencies. European Research Studies Journal, 23(2), 352–361. https://doi.org/10.35808/ersj/1828.
Rabiner, L., Rosenberg, A., & Levinson, S. (1978). Considerations in dynamic time warping algorithms for discrete word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(6), 575–582. https://doi.org/10.1109/tassp.1978.1163164.
Rešovský, M., Horváth, D., Gazda, V., & Siničáková, M. (2013). Minimum Spanning Tree Application in the Currency Market. Biatec, 21(7), 21–23. https://www.nbs.sk/_img/Documents/_PUBLIK_NBS_FSR/Biatec/Rok2013/07-2013/05_biatec13-7_resovsky_EN.pdf.
Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43–49. https://doi.org/10.1109/tassp.1978.1163055.
Salvador, S., & Chan, P. (2007). FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. Intelligent Data Analysis, 11(5), 70–80. https://doi.org/10.3233/IDA-2007-11508.
Sardá-Espinosa, A. (2019). Time-series clustering in R using the dtwclust package. R Journal, 11(1), 22–43. https://doi.org/10.32614/RJ-2019-023.
Sharma, G. D., Tiwari, A. K., Jain, M., Yadav, A., & Erkut, B. (2021). Unconditional and conditional analysis between Covid-19 cases, temperature, exchange rate and stock markets using wavelet coherence and wavelet partial coherence approaches. Heliyon, 7(2), 1–30. https://doi.org/10.1016/j.heliyon.2021.e06181.
Silva, D. F., Batista, G. E. A. P. A., & Keogh, E. (2016). On the effect of endpoints on dynamic time warping (SIGKDD Workshop on Mining and Learning from Time Series, II). https://core.ac.uk/download/pdf/78275924.pdf.
Stübinger, J. (2019). Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance, 19(6), 921–935. https://doi.org/10.1080/14697688.2018.1537503.
Stübinger, J., & Schneider, L. (2020). Epidemiology of coronavirus COVID-19: Forecasting the future incidence in different countries. Healthcare, 8(2), 1–15. https://doi.org/10.3390/healthcare8020099.
Tappert, C. C., Suen, C. Y., & Wakahara, T. (1990). The state of the art in online handwriting recognition. Pattern Analysis and Machine Intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8), 787–808. https://doi.org/10.1109/34.57669.
Villarreal-Samaniego, D. (2020). COVID-19, Oil Prices, and Exchange Rates: A Five-Currency Examination. http://dx.doi.org/10.2139/ssrn.3593753.
Wang, G.-J., 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, 391(16), 4136–4146. https://doi.org/10.1016/j.physa.2012.03.036.
Wang, G.-J., Xie, C., Chen, Y.-J., & Chen, S. (2013). Statistical Properties of the Foreign Exchange Network at Different Time Scales: Evidence from Detrended Cross-Correlation Coefficient and Minimum Spanning Tree. Entropy, 15(5), 1643–1662. https://doi.org/10.3390/e15051643.
