Journal of Applied Economic Research
ISSN 2712-7435
Dynamic Connectedness between Crypto and Conventional Financial Assets: Novel Findings from Russian Financial Market
Mirzat Ullah
Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia
Abstract
In the dynamic landscape of the Russian digital economy and increasing financial openness, crypto assets have emerged as influential players in the financial market. The geopolitical and economic developments after a conflict with Ukraine have presented formidable challenges in the shape of financial and trade sanctions, coupled with the suspension from the SWIFT banking system, has plunged the Russian economy into a precarious situation. The current study delves into the network spillover effects between a prominent crypto asset and various financial assets including equity, exchange rates, crude oil, gold, and commodity futures using daily data from January 01, 2018, to August 31, 2023. The purpose of the study is to provide empirical and theoretical insights into countering the impact of sanctions on Russia, proposing a pragmatic solution for the Russian financial market. The research methodology involves the application of network spillover estimation and value-at-risk analysis. Notably, the findings expose a robust association between crypto and financial assets, where crypto assets play a pivotal role in transmitting risk within the financial landscape. While their impact on other financial assets remains relatively subdued, short-term correlations exhibit volatile fluctuations, often marked by sharp increases in downside risk. Theoretical implications follow the portfolio theory of asset pricing, with extreme risk spillover originating from long-run fluctuations in the crypto market, impacting market sentiment and elevating risk propagation in the Russian financial market. These results carry practical significance for payment and receipt processes, as well as trading activities with foreign countries, presenting essential insights for policymakers and investment decision-makers.
Keywords
crypto assets; financial assets; Russian financial market; network spillover analysis.
JEL classification
C31, G11, G12References
1. Ullah, M., Sohag, K., Khan, S., Sohail, H.M. (2023). Impact of Russia–Ukraine conflict on Russian financial market: Evidence from TVP-VAR and quantile-VAR analysis. Russian Journal of Economics, Vol. 9, No. 3, 284–305. doi.org/10.32609/j.ruje.9.105833
2. Fang, Y., Shao, Z. (2022). The Russia-Ukraine conflict and volatility risk of commodity markets. Finance Research Letters, Vol. 50, 103264. doi.org/10.1016/j.frl.2022.103264
3. Aysan, A.F., Demir, E., Gozgor, G., Lau, C.K.M. (2019). Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business and Finance, Vol. 47, 511–518. doi.org/10.1016/j.ribaf.2018.09.011
4. Girardone, C. (2022). Russian Sanctions and the Banking Sector. British Journal of Management, Vol. 33, Issue 4, 1683–1688. doi.org/10.1111/1467-8551.12656
5. Yang, Y. (2023). Effect of Ukraine-Russia Conflict on the Cryptocurrency Market: an Event Study Perspective. BCP Business & Management, Vol. 38, 181–187. doi.org/10.54691/bcpbm.v38i.3686
6. Theiri, S., Nekhili, R., Sultan, J. (2023). Cryptocurrency liquidity during the Russia–Ukraine war: the case of Bitcoin and Ethereum. Journal of Risk Finance, Vol. 24, Issue 1, 59–71. doi.org/10.1108/JRF-05-2022-0103
7. Allen, F., Fatas, A., Weder Di Mauro, B. (2022). Was the ICO boom just a sideshow of the Bitcoin and Ether Momentum? Journal of International Financial Markets, Institutions and Money, Vol. 80, 101637. doi.org/10.1016/j.intfin.2022.101637
8. Mensi, W., Gubareva, M., Ko, H.-U., Vo, X.V., Rang, S.H. (2023). Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets. Financial Innovation, Vol. 9, Issue 1, 92. doi.org/10.1186/s40854-023-00498-y
9. Wang, Q., Wei, Y., Wang, Y., Liu, Y. (2022). On the Safe-Haven Ability of Bitcoin, Gold, and Commodities for International Stock Markets: Evidence from Spillover Index Analysis. Discrete Dynamics in Nature and Society, Vol. 2022, 9520486. doi.org/10.1155/2022/9520486
10. Polat, O. (2023). Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks. Anadolu Üniversitesi Sosyal Bilimler Dergisi, Vol. 23, Issue 1, 29–50. https://doi.org/10.18037/ausbd.1272534
11. Kumar, S., Patel, R., Iqbal, N., Gubareva, M. (2023). Interconnectivity among cryptocurrencies, NFTs, and DeFi: Evidence from the Russia-Ukraine conflict. North American Journal of Economics and Finance, Vol. 68, 101983. doi.org/10.1016/j.najef.2023.101983
12. Ullah, M., Sohail, H.M., Haddad, H., Al-Ramahi, N.M., Khan, M.A. (2023). Global Structural Shocks and FDI Dynamic Impact on Productive Capacities: An Application of CS-ARDL Estimation. Sustainability, Vol. 15, Issue 1, 283. doi.org/10.3390/su15010283
13. Sohag, K., Ullah, M. (2022). Response of BTC Market to Social Media Sentiment: Application of Cross-Quantilogram with Bootstrap. In: Digitalization and the Future of Financial Services. Contributions to Finance and Accounting. Edited by D.B. Vukovic, M. Maiti, E.M. Grigorieva. Springer, Cham., 103–119. doi.org/10.1007/978-3-031-11545-5_6
14. Ali, F., Bouri, E., Naifar, N., Shahzad, S.J.H., AlAhmad, M. (2022). An examination of whether gold-backed Islamic cryptocurrencies are safe havens for international Islamic equity markets. Research in International Business and Finance, Vol. 63, 101768. doi.org/10.1016/j.ribaf.2022.101768
15. Raza, S.A., Ahmed, M., Aloui, C. (2022). On the asymmetrical connectedness between cryptocurrencies and foreign exchange markets: Evidence from the nonparametric quantile on quantile approach. Research in International Business and Finance, Vol. 61, 101627. doi.org/10.1016/j.ribaf.2022.101627
16. Bouri, E., Gupta, R., Vo, X.V. (2022). Jumps in Geopolitical Risk and the Cryptocurrency Market: The Singularity of Bitcoin. Defence and Peace Economics, Vol. 33, Issue 2, 150–161. doi.org/10.1080/10242694.2020.1848285
17. Baur, D.G., Hong, K., Lee, A.D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, Vol. 54, 177–189. doi.org/10.1016/j.intfin.2017.12.004
18. Dyhrberg, A.H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, Vol. 16, 85–92. https://doi.org/10.1016/j.frl.2015.10.008
19. Dyhrberg, A.H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, Vol. 16, 139–144. doi.org/10.1016/j.frl.2015.10.025
20. Bouri, E., Shahzad, S.J.H., Roubaud, D., Kristoufek, L., Lucey, B. (2020). Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. Quarterly Review of Economics and Finance, Vol. 77, 156–164. doi.org/10.1016/j.qref.2020.03.004
21. Yousaf, I., Plakandaras, V., Bouri, E., Gupta, R. (2023). Hedge and safe-haven properties of FAANA against gold, US Treasury, bitcoin, and US Dollar/CHF during the pandemic period. North American Journal of Economics and Finance, Vol. 64, 101844. doi.org/10.1016/j.najef.2022.101844
22. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., Hagfors, L.I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, Vol. 20, 192–198. doi.org/10.1016/j.frl.2016.09.025
23. Khan, S., Ullah, M., Shahzad, M.R., Khan, U.A., Khan, U., Eldin, S.M., Alotaibi, A.M. (2022). Spillover Connectedness among Global Uncertainties and Sectorial Indices of Pakistan: Evidence from Quantile Connectedness Approach. Sustainability, Vol. 14, Issue 23, 15908. doi.org/10.3390/su142315908
24. Koutmos, D. (2018). Return and volatility spillovers among cryptocurrencies. Economics Letters, Vol. 173, 122–127. doi.org/10.1016/j.econlet.2018.10.004
25. Abubakr Naeem, M., Iqbal, N., Lucey, B.R., Karim, K. (2022). Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data. Journal of International Financial Markets, Institutions and Money, Vol. 81, 101695. doi.org/10.1016/j.intfin.2022.101695
26. Yousaf, I., Jareño, F., Martínez-Serna, M.-I. (2023). Extreme spillovers between insurance tokens and insurance stocks: Evidence from the quantile connectedness approach. Journal of Behavioral and Experimental Finance, Vol. 39, 100823. doi.org/10.1016/j.jbef.2023.100823
27. Hassan, M.K., Hasan, M.B., Halim, Z.A., Maroney, N., Rashid, M.D. (2022). Exploring the dynamic spillover of cryptocurrency environmental attention across the commodities, green bonds, and environment-related stocks. North American Journal of Economics and Finance, Vol. 61, 101700. doi.org/10.1016/j.najef.2022.101700
28. Elsayed, A.H., Sousa, R.M. (2022). International monetary policy and cryptocurrency markets: dynamic and spillover effects. European Journal of Finance, 1–21. doi.org/10.1080/1351847X.2022.2068375
29. Wang, G.-J., Xie, C., Wen, D., Zhao, L. (2019). When Bitcoin meets economic policy uncertainty (EPU): Measuring risk spillover effect from EPU to Bitcoin. Finance Research Letters, Vol. 31, S1544612318305749. doi.org/10.1016/j.frl.2018.12.028
30. Kristoufek, L. (2023). Will Bitcoin ever become less volatile? Finance Research Letters, Vol. 51, 103353. doi.org/10.1016/j.frl.2022.103353
31. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, Vol. 158, 3–6. doi.org/10.1016/j.econlet.2017.06.023
32. Beraich, M., Amzile, K., Laamire, J., Zirari, O., Fadali, M.A. (2022). Volatility Spillover Effects of the US, European and Chinese Financial Markets in the Context of the Russia–Ukraine Conflict. International Journal of Financial Studies, Vol. 10, Issue 4, 95. doi.org/10.3390/ijfs10040095
33. Ali, F., Jiang, Y., Sensoy, A. (2021). Downside risk in Dow Jones Islamic equity indices: Precious metals and portfolio diversification before and after the COVID-19 bear market. Research in International Business and Finance, Vol. 58, 101502. doi.org/10.1016/j.ribaf.2021.101502
34. Conlon, T., McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the Covid-19 bear market. Finance Research Letters, Vol. 35, 101607. doi.org/10.1016/j.frl.2020.101607
35. Taera, E.G., Setiawan, B., Saleem, A., Wahyuni, A.S., Chang, D.K.S., Nathan, R.J., Lakner, Z. (2023). The impact of Covid-19 and Russia–Ukraine war on the financial asset volatility: Evidence from equity, cryptocurrency and alternative assets. Journal of Open Innovation: Technology, Market, and Complexity, Vol. 9, Issue 3, 100116. doi.org/10.1016/j.joitmc.2023.100116
36. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, Vol. 7, Issue 1, 77–91. doi.org/10.1111/j.1540-6261.1952.tb01525.x
37. Bardou, O., Frikha, N., Pages, G. (2009). Computation of VaR and CVaR using stochastic approximations and unconstrained importance sampling. Monte Carlo Methods and Applications, Vol. 15, Issue 3, 173–210. doi.org/10.1515/MCMA.2009.011
38. Diebold, F.X., Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, Vol. 28, Issue 1, 57–66. doi.org/10.1016/j.ijforecast.2011.02.006
39. Glosten, L.R., Jagannathan, R., Runkle, D.E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, Vol. 48, Issue 5, 1779–1801. doi.org/10.1111/j.1540-6261.1993.tb05128.x
40. Lai, T.L., Xing, H. (2013). Stochastic change-point ARX-GARCH models and their applications to econometric time series. Statistica Sinica, Vol. 23, No. 4, 1573–1594. http://dx.doi.org/10.5705/ss.2012.224s
41. Engle, R.F., Ng, V.K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, Vol. 48, Issue 5, 1749–1778. doi.org/10.1111/j.1540-6261.1993.tb05127.x
42. Raza, S.A., Shah, N., Guesmi, K., Msolli, B. (2022). How does COVID-19 influence dynamic spillover connectedness between cryptocurrencies? Evidence from non-parametric causality-in-quantiles techniques. Finance Research Letters, Vol. 47, 102569. doi.org/10.1016/j.frl.2021.102569
43. Kristoufek, L., Bouri, E. (2023). Exploring sources of statistical arbitrage opportunities among Bitcoin exchanges. Finance Research Letters, Vol. 51, 103332. doi.org/10.1016/j.frl.2022.103332
About Authors
Mirzat Ullah
Post-Graduate Student, Department of Economics, Research Engineer, Laboratory of International and Regional Economics, School of Economics and Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID orcid.org/0000-0003-1517-0611 e-mail: mirzat.ullakh@urfu.ru
For citation
Ullah, M. (2024). Dynamic Connectedness between Crypto and Conventional Financial Assets: Novel Findings from Russian Financial Market. Journal of Applied Economic Research, Vol. 23, No. 1, 110-135. doi.org/10.15826/vestnik.2024.23.1.005
Article info
Received November 23, 2023; Revised December 19, 2023; Accepted January 8, 2024.
DOI: https://doi.org/10.15826/vestnik.2024.23.1.005
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