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

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