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Assessment of the Volatility of the Main Cryptocurrencies, the Euro and the Direct Exchange Rate of the Ruble

https://doi.org/10.26794/2587-5671-2024-28-1-133-144

Abstract

The development of financial technologies in modern conditions has contributed to the active use of digital financial instruments — cryptocurrencies — in international settlements. The availability of up-to-date information on digital currency volatility will help crypto market participants predict the consequences of their transactions. The purpose of this work is to construct a new measure of the volatility of financial assets, in particular, cryptocurrencies, the euro and the direct exchange rate of the ruble. In order to obtain this measure, an analysis of known volatility measures was carried out, requirements for the measure of volatility of a financial asset were formulated, and, as a result, the volatility of the main cryptocurrencies, the euro and the direct exchange rate of the ruble, was assessed by the levels of the time series of monthly quotations of these assets in the time interval from 1.01.2022 to 1.04.2023. The scientific novelty in the paper is a reasonable new measure of absolute volatility. The main conclusions of the study are: 1) the measure of absolute volatility constructed in this paper has the dimension of the asset value and measures the part of the asset value that is generated by uncertainty in the values of its profitability; 2) Bitcoin Cash is the most volatile cryptocurrency, Bitcoin has the least volatility among cryptocurrencies; 3) the volatility of the direct exchange rate of the ruble (the price of the US dollar in rubles) is about half the volatility of Bitcoin; 4) out of competition in terms of volatility is the euro quote (the euro price in dollars) — 10% in a year and a half.

About the Authors

V. A. Byvshev
Financial University
Russian Federation

Victor A. Byvshev — Dr. Sci. (Tech.), Prof., Department of Information Technology and Big Data Analysis

Moscow



M. A. Yashchenko
Financial University
Russian Federation

Nataliya A. Yashchenko — Assoc. Prof., Department of Information Technology and Big Data Analysis

Moscow



References

1. Phillips P.C.B., Shi S., Yu J. Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review. 2015;56(4):1043–1078. DOI: 10.1111/iere.12132

2. Filimonov V., Sornette D. A stable and robust calibration scheme of the log-periodic power law model. Physica A: Statistical Mechanics and its Applications. 2013;392(17):3698–3707. DOI: 10.1016/j.physa.2013.04.012

3. Geuder J., Kinateder H., Wagner N.F. Cryptocurrencies as financial bubbles: The case of Bitcoin. Finance Research Letters. 2019;31. DOI: 10.1016/j.frl.2018.11.011

4. Enoksen F.A., Landsnes Ch.J., Lučivjanská K., Molnár P. Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behavior and Organization. 2020;176:129–144. DOI: 10.1016/j.jebo.2020.05.005

5. Zhang J., Xu Y., Wang H. Cryptocurrency price bubble detection using log-periodic power law model and wavelet analysis. SSRN Electronic Journal. 2021. DOI: 10.2139/ssrn.3983539

6. Kyriazis N., Papadamou S., Corbet S. A systematic review of the bubble dynamics of cryptocurrency prices. Research in International Business and Finance. 2020;54:101254. DOI: 10.1016/j.ribaf.2020.101254

7. Caferra R., Tedeschi G., Morone A. Bitcoin: Bubble that bursts or Gold that glitters? Economics Letters. 2021;205:109942. DOI: 10.1016/j.econlet.2021.109942

8. Wheelan Ch. Naked money: A revealing look at our financial system. New York, NY: W.W. Norton & Co.; 2017. 368 p. (Russ. ed.: Wheelan Ch. Golye den’gi: otkrovennaya kniga o finansovoi sisteme. Moscow: Mann, Ivanov and Ferber; 2022. 384 p.).

9. Krylov G.O., Lisitsyn A. Yu., Polyakov L.I. Comparative analysis of volatility of cryptocurrencies and fiat money. Finance: Theory and Practice. 2018;22(2):66–89. (In Russ.). DOI: 10.26794/2587–5671–2018–22–2–66–89

10. Andersen T.G., Bollerslev T. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review. 1998;39(4):885–905. DOI: 10.2307/2527343

11. Kussy M. Yu. Metodological characteristics of volatility assessment. Uchenye zapiski Krymskogo federal’nogo universiteta imeni V.I. Vernadskogo. Ekonomika i upravlenie = Scientific Notes of V.I. Vernadsky Crimean Federal University. Economics and Management. 2018;4(1):59–78. URL: https://cyberleninka.ru/article/n/metodologicheskie-aspekty-izmereniya-volatilnosti (In Russ.).

12. Aganin A. D., Peresetsky A. A. Volatility of ruble exchange rate: Oil and sanctions. Prikladnaya ekonometrika = Applied Econometrics. 2018;(4):5–21. URL: https://cyberleninka.ru/article/n/volatilnostkursa-rublya-neft-i-sanktsii (In Russ.).

13. Aganin A., Manevich V., Peresetsky A., Pogorelova P. Comparison of cryptocurrency and stock market volatility forecast models. Ekonomicheskii zh urnal Vysshei shkoly ekonomiki = The HSE Economic Journal. 2023;27(1):49–77. (In Russ.). DOI: 10.17323/1813–8691–2023–27–1–49–77

14. Barndorff-Nielsen O.E., Shephard N. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. 2002;64(2):253–280. DOI: 10.1111/1467–9868.00336

15. Byvshev V. A. Econometrics. Moscow: Finansy i statistika; 2008. 480 p. (In Russ.).

16. Byvshev V. A. Modeling of fi nancial and economic time series in R. Moscow: Financial Univers ity under the Government of the Russian Federation; 2019. 110 p. (In Russ.).


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Byvshev V.A., Yashchenko M.A. Assessment of the Volatility of the Main Cryptocurrencies, the Euro and the Direct Exchange Rate of the Ruble. Finance: Theory and Practice. 2024;28(1):133-144. https://doi.org/10.26794/2587-5671-2024-28-1-133-144

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ISSN 2587-5671 (Print)
ISSN 2587-7089 (Online)