Аuthor: O. Liashenko, Dr. Sci. (Econ.), Prof., ORCID ID: 0000-0002-0197-4179,
T. Kravets, PhD (Phys. & Math.), Associate Prof., ORCID ID: 0000-0003-4823-5143
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine,
K. Petrenko, Product Marketing Manager
BACOTECH, Kyiv, Ukraine
Annotation: Modern conditions for the development of international market relations and participation in world globalization processes necessitate
strengthening the monetary and credit system, increasing the effectiveness of the use of monetary policy tools to enhance their influence on
restructuring and further development of the economy. In the rapid advance of information technologies, new economic management tools, including
electronic money, appear every day. The emergence of new types of financial instruments, such as cryptocurrencies, is due to globalization in the
financial market.
The work aims to identify and model the mutual influence of indicators’ returns, comparing the dynamic characteristics of the cryptocurrency
market with some traditional and widely used stock indices, taking into account other factors, for example, the global crisis situation. It was established that three powerful stock indices (S&P 500, Dow Jones, NASDAQ Composite) show a low level of interdependence of volatility
evolutions with cryptocurrencies. However, the WLMC construction proved Bitcoin’s dependence on leading stock indices on all scales during 2020–
2021. Also, WLMC’s high performance on the first 100-day period (January–April 2020) and a two-week scale during November 2021 – January 2022 are in the combination of cryptocurrencies with the S&P 500 stock index. The created VAR models demonstrate positive interdependence between Bitcoin and the S&P 500. The study of the ensemble of time series showed that at times of disturbances, resonant phenomena manifest in the behavior of returns of stock indices and cryptocurrencies. The leading role is played by the S&P 500 stock index, while the similar behavior of Bitcoin is manifested indirectly.
Keywords: cryptocurrencies, stock indices, discrete wavelet transform (DWT), maximum overlap discrete wavelet transform (MODWT), local multiple wavelet correlation (WLMC), VAR models.
Received: 19/10/2022
1st Revision: 08/11/2022
Accepted: 30/11/2022
DOI: https://doi.org/10.17721/1728-2667.2022/221-4/5
References (in Latin): Translation / Transliteration/ Transcription
1. Cryptocurrency vs. Stocks: Understanding the differences. URL: https://online.maryville.edu/blog/cryptocurrency-vs-stocks/ (дата звернення
10.04.2022)
2. Cryptocurrency vs. Stocks: what’s the better choice? URL: https://n26.com/en-eu/blog/crypto-vs-stocks (дата звернення 12.04.2022)
3. Liang, J., Li, L., Chen, W., Zeng, D. (2019). Towards an understanding of cryptocurrency: a comparative analysis of cryptocurrency,
foreign exchange, and stock. IEEE International Conference on Intelligence and Security Informatics (ISI). doi: 10.1109/ISI.2019.8823373
4. Caferra, R., Vidal-Tomás, D. (2021). Who raised from the abyss? A comparison between cryptocurrency and stock market dynamics during the
COVID-19 pandemic. Finance Research Letters, Vol. 43. doi: 10.1016/j.frl.2021.101954
5. Schilling, L., and Uhlig, H. (2019). Some simple bitcoin economics. Journal of Monetary Economics. Vol. 106. doi:10.1016/j.jmoneco.2019.07.002
6. Daubechies, I. (1992). Ten lectures on wavelets. Society for industrial and applied mathematics.
7. Fernández-Macho, J. (2018). Time-localized wavelet multiple regression and correlation. Phys. A Stat. Mech. Appl. 492. doi:10.1016/
j.physa.2017.11.050
8. Goupillaud, P., Grossmann, A., Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23(1).
9. Polanco-Martínez, J. M., Fernández-Macho, J., Medina-Elizalde, M. (2020). Dynamic wavelet correlation analysis for multivariate climate time
series. Scientific Reports 10 (21277). doi: 10.1038/s41598-020-77767-8
10. The 2021 Global crypto adoption index. URL: https://blog.chainalysis.com/reports/2021-global-crypto-adoption-index/ (дата звернення 12.04.2022)
11. Kumar, A. S., Ajaz, T. (2019). Co-movement in crypto-currency markets: evidences from wavelet analysis. Financial Innovation. 5:33.
doi:10.1186/s40854-019-0143-3
12. Liu, J., Serletis, A. (2019). Volatility in the cryptocurrency market. Open Econ Rev 30 (4). doi:10.1007/s11079-019-09547-5
13. Yuneline, M. H. (2019). Analysis of cryptocurrency’s characteristics in four perspectives. Journal of Asian Business and Economic Studies.
Vol. 26. No. 2. doi:10.1108/JABES-12-2018-0107
14. Liashenko, O., Kravets, T., Repetskiyi, Y. (2020). Neural Networks in Application to Cryptocurrency Exchange Modeling. 7th International
Conference “Information Technology and Interactions” (IT&I-2020). Workshops Proceedings. Vol. 2845. http://ceur-ws.org/Vol-2845/Paper_32.pdf
15. Liashenko, O., Kravets, T., Filogina, A. (2020). Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks.
Ekonomika. Vol. 99 (2). doi:10.15388/Ekon.2020.2.2
16. Liashenko, O., Kravets, T., Repetskiyi, Y. (2020). Application of Artificial Intelligence to Bitcoin Course Modelling. Вісник КНУ ім. Тараса Шевченка. Економіка. № 2 (209). doi: 10.17721/1728-2667.2020/209-2/2
17. Goodell, J. W., Goutte, S. (2021). Co-movement of COVID-19 and bitcoin: Evidence from wavelet coherence analysis. Finance Research
Letters, Vol. 38. doi:10.1016/j.frl.2020.101625
18. Omane-Adjepong, M., Alagidede, I. P., Dramani, J. B. (2020). COVID-19 Outbreak and Co-Movement of Global Markets: Insight from
Dynamic Wavelet Correlation Analysis. In: Wavelet Theory [Internet]. London: IntechOpen. doi: 10.5772/intechopen.95098
19. Gallegati, M. (2007). Wavelet analysis of stock returns and aggregate economic activity. Computational Statistics & Data Analysis,
Vol. 50 (6). doi:10.1016/j.csda.2007.07.019
20. Charfeddine, L. et al. (2019). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial
investors. Economic Modelling. doi:10.1016/j.econmod.2019.05.016
21. Watorek, M., Kwapien, J., Drozdz, S. (2022). Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19
Time. Future Internet, 14 (215). doi:10.3390/fi14070215
22. Colonescu, C. (2016). Principles of Econometrics with R. URL: https://bookdown.org/ccolonescu/RPoE4/
23. Bitcoin. URL: https://www.investing.com/crypto/bitcoin (дата звернення 10.01.2022)
24. Ethereum. URL: https://www.investing.com/crypto/ethereum (дата звернення 12.01.2022)
25. BNB. URL: https://www.investing.com/crypto/bnb (дата звернення 11.01.2022)
26. S&P 500. URL: https://www.investing.com/indices/us-spx-500 (дата звернення 11.01.2022)
27. Dow Jones Industrial Average. URL: https://www.investing.com/indices/us-30 (дата звернення 12.01.2022)
28. NASDAQ Composite. URL: https://www.investing.com/indices/nasdaq-composite (дата звернення 11.01.2022)