Issue page

DOI: 10.47026/2499-9636-2025-2-1-14

Arkadeva O.G., Kreminsky P.I.

Comparative assessment of realized volatility of the dollar and the yuan in the financial market of the Russian Federation

Keywords: volatility factors, exchange rates, dollar dominance, yuanization, GARCH-models, volatility spillovers, volatility peaks

In the context of post-pandemic macroeconomic instability, the study of currency volatility is of particular importance for financial analysis and investment decision-making. Volatility essentially reflects the degree of macroeconomic and financial uncertainty and the degree of reaction to various events in the prices of exchange-traded assets, which makes it relevant to study the realized volatility of key currencies for the Russian market at the present stage. The purpose of the study is to identify the volatility factors of the dollar and the yuan and to compare the degree of realized volatility of these currencies in the Russian financial market in the post-pandemic period, characterized by a consistent succession of macroeconomic instability periods. Materials and methods. Pandas, Numpy, Arch, Matplotlib, and Openpyxl libraries were imported into the Python software development and execution environment in the cloud – Google Colab. To test the hypothesis of the study and analyze the volatility of the exchange rate, monthly data on the USD/RUB and CNY/RUB currency pairs downloaded from the website investing.com were used. The observation period is from January 1, 2020 to April 1, 2025. Based on these data, a GARCH model was created in the Google Colab environment, its parameterization and adjustment were carried out. Later, the results of GARCH modeling were supplemented by an analysis of the fundamental factors in range of currencies. Results. The conducted research makes it possible to differentiate the factors of realized volatility of the US dollar and the Chinese yuan in the Russian financial market in the period from 2020 to 2025, covering significant macroeconomic, political and market transformations. The GARCH model confirmed the presence of a significantly larger amplitude of fluctuations in the USD/RUB exchange rate compared to CNY/RUB. Particularly sharp plummetings were recorded in March 2020, February 2022 and August 2024, when the dollar showed an acute reaction to foreign policy and sanctions events. Under these conditions, the yuan's exchange rate maintained its relative stability, demonstrating less sensitivity to macroeconomic triggers, which led to lower realized volatility. Conclusions. The volatility of the dollar in the Russian market is significantly higher, especially during periods of crisis shocks (for example, February–May 2022), reflecting its high dependence on foreign policy factors and international sanctions restrictions. The yuan is characterized by lower realized volatility, which, in conditions of economic instability, increases its attractiveness for use for settlement and hedging purposes, subject to the development of appropriate tools. The above confirms the initial hypothesis about a greater stability of the yuan compared to the US dollar, but this stability is of a non–market nature. The analysis of volatility peaks periods made it possible to link market reactions to specific macroeconomic and political events, including the pandemic, sanctions packages, energy crises and policy changes of the Bank of Russia. The results obtained emphasize the relevance of diversifying currency risks in the context of macroeconomic instability and expanding the range of alternative currencies (in particular, the yuan) in settlement operations and investment practice in the Russian market.

References

  1. Dollar SSHA/Rossiiskii rubl’ [USD/RUB – US Dollar/Russian Ruble]. Available at: https://ru.investing.com/currencies/usd-rub (Access Date: 2025, Apr. 29).
  2. Zharikov M.V. Analiz faktorov, uslovii i perspektiv povysheniya roli yuanya v mirovoi valyutnoi sisteme [Analysis of factors, conditions and prospects for increasing the role of the yuan in the global monetary system]. Mirovaya ekonomika i mirovye finansy, 2023, vol. 2, no. 1, pp. 5–14. DOI 10.24412/2949-6454-2023-0010.
  3. Zhuravleva T.V. Analiz povedeniya volatil’nosti virtual’nykh valyut pri pomoshchi odnomernykh modelei GARCH [Analysis of the behavior of volatility of virtual currencies using one-dimensional GARCH models]. Naukosfera, 2024, no. 4-2, pp.361– DOI 10.5281/zenodo.11120662.
  4. Kitaiskii yuan’/Dollar SSHA [CNY/USD – Chinese Yuan/US Dollar]. Available at: https://ru.investing.com/currencies/cny-usd-chart (Access Date: 2025, Apr. 29).
  5. Kross-kursy valyut. CNY [Cross rates. CNY]. Available at: https://ru.investing.com/currencies/single-currency-crosses?currency=cny (Access Date: 2025, Apr. 29).
  6. Lizun E.I., Shershkina A.V., Kostina O.I. Yuan’ kak novaya valyuta v rossiiskoi ekonomike [Yuan as a new currency in the Russian economy]. Vestnik Altaiskoi akademii ekonomiki i prava, 2024, no. 11-1, pp. 64–68. DOI 10.17513/vaael.3822.
  7. Perskaya V.V. Mozhet li yuan’ zamestit’ dollar v mezhdunarodnykh ekonomicheskikh otnosheniyakh? [Can the Yuan Replace the Dollar in International Economic Relations?]. Problemy natsionalnoi strategii, 2023, no. 5(80), pp. 144–171. DOI52311/2079-3359_2023_5_144.
  8. Torgovaya voina s SSHA zastavila Kitai prodvigat’ tsifrovoi yuan’ [The trade war with the US has forced China to promote the digital yuan.]. Available at: https://ru.investing.com/news/cryptocurrency-news/article-2718720 (Access Date: 2025, Apr. 29).
  9. Alfeus M., Harvey J., Maphatsoe P. Improving realised volatility forecast for emerging markets. J Econ Finan, 2025, no. 49, pp. 299–342. DOI: 1007/s12197-024-09701-x.
  10. Escobar-Anel M., Spies B., Zagst, R. Optimal consumption and investment in general affine GARCH models. OR Spectrum, 2024, no. 46, pp. 987–1026. DOI: 10.1007/s00291-024-00749-z.
  11. Fang Z., Han JY. Realized GARCH Model in Volatility Forecasting and Option Pricing. Comput Econ, 2025. DOI: 10.1007/s10614-024-10826-8.
  12. Matsui T., Knottenbelt W.J. Forecasting Realised Volatility: Implied and GARCH Volatility in Bitcoin, Gold, Oil Markets. In: Mathematical Research for Blockchain Economy. MARBLE 2024. Lecture Notes in Operations Research. Springer, Cham, 2024, pp. 113–128. DOI: 1007/978-3-031-68974-1_6.
  13. RMB Tracker. Swift. Available at: https://www.swift.com/sites/default/files/files/rmb-tracker_april-2025-1.pdf.
  14. Stavrakeva V., Tang J. Explaining the great moderation exchange rate volatility puzzle. IMF Econ Rev, 2025, no. 73, pp. 196–238. DOI: 1057/s41308-024-00264-9.
  15. Takahashi M., Omori Y., Watanabe T. Stochastic volatility and realized stochastic volatility models. Springer Singapore, 2023, 113 p. DOI: 10.1007/978-981-99-0935-3.
  16. Tang S.H., Rosenbaum M., Zhou C. Forecasting volatility with machine learning and rough volatility: example from the crypto-winter. Digit Finance, 2024, vol. 6, pp. 639–655. DOI: 10.1007/s42521-024-00108-1.
  17. Vo M. Measuring and Forecasting Stock Market Volatilities with High-Frequency Data. Comput Econ, 2024. DOI: 1007/s10614-024-10674-6.
  18. Wang Q., Yao Z. Bayesian influence diagnostics for a multivariate GARCH model. Stat Papers, 2025, no. 66, p. 35. DOI: 1007/s00362-024-01649-8.
  19. Yıldırım H., Bekun F.V. Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models. Futur Bus J, 2023, no. 9, p. 75. DOI: 10.1186/s43093-023-00255-8.

About authors

Arkadeva Olga G.
Candidate of Economics Sciences, Associate Professor, Department of Finance, Credit and Economic Security, Chuvash State University, Russia, Cheboksary (knedlix@yandex.ru; ORCID: https://orcid.org/0000-0003-4868-2365)
Kreminsky Pavel I.
Specialist of GDP_04, VTB Bank (JSPC), Russia, Cheboksary (pavelhulk58@mail.ru; )

Article link

Arkadeva O.G., Kreminsky P.I. Comparative assessment of realized volatility of the dollar and the yuan in the financial market of the Russian Federation [Electronic resource] // Oeconomia et Jus. – 2025. – №2. P. 1-14. – URL: https://oecomia-et-jus.ru/en/single/2025/2/1/. DOI: 10.47026/2499-9636-2025-2-1-14.