ejebsEurasian Journal of Economic and Business StudiesНазвание журнала на русском2789-82532789-8261University of International Business named after K. Sagadiyev LLP10.47703/2789-8253-2026-2-24-36ejebs-279Research ArticleСтатьиVolatility Spillovers in the Economics of Cryptocurrencies and Financial Marketshttps://orcid.org/0000-0002-4918-4598CelikS.

Saban Celik, PhD, Associate Professor

Izmir

saban.celik@ikcu.edu.trhttps://orcid.org/0000-0003-3561-4996DumanA.

Aslı Duman, PhD Student

Izmir

asli.duman@ikcu.edu.trIzmir Katip Celebi UniversityTurkey2026050720267022436Copyright © Celik S., Duman A., 20262026Celik S., Duman A.Celik S., Duman A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ejebs.uib.kz/jour/article/view/279

   With the increasing role of cryptocurrencies in the global financial system, the analysis of the mechanisms by which volatility transfers between digital and traditional assets is becoming particularly relevant.

   The purpose of the study is to assess the degree, directions, and temporal variability of volatility spillovers between Bitcoin, leading cryptocurrencies, and traditional financial instruments.

   Using daily data and both static and rolling-window estimates, the analysis assesses how shocks to volatility are transmitted between cryptocurrency and macro-financial markets. The results of the study showed that the Total Spillover Index (TSI) in the static model is 28.37 %, while in the dynamic model it reaches an average of 35.9 %, with peak values up to 45.25 % in 2022. It has been established that Bitcoin acts as a net transmitter of volatility: the average level of transmitted effects is 60.31 %, received effects are 50.59 %, and the net spillover is +9.72 %. Moreover, Bitcoin’s place in the network is state-dependent to some extent: while it acts as a net transmitter in the average, speculative episodes in alternative cryptocurrencies can cause Bitcoin to act as a receiver of volatility shocks. The results indicate a high degree of internal connectivity in the cryptocurrency market, with limited integration with the traditional financial system, and a pronounced temporary variability in the structure of volatility interactions. These findings have implications for portfolio diversification, risk management, and the ongoing integration of digital assets into the global financial system.

VolatilityVolatility TransmissionBitcoinRiskCryptocurrencyDigital EconomyFinancial EconomicsReferencesAdelopo, I., & Luo, X. (2025). Interconnectedness among cryptocurrencies and financial markets : a systematic literature review. Digital Finance, 7, 1119–1171. doi: 10.1007/s42521-025-00155-2Adelopo, I., & Luo, X. (2025). Interconnectedness among cryptocurrencies and financial markets : a systematic literature review. Digital Finance, 7, 1119–1171. doi: 10.1007/s42521-025-00155-2Bouri, 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, 20, 192–198. doi: 10.1016/j.frl.2016.09.025Bouri, 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, 20, 192–198. doi: 10.1016/j.frl.2016.09.025Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. doi: 10.1016/j.econlet.2018.01.004Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. doi: 10.1016/j.econlet.2018.01.004Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. doi: 10.1111/j.1468-0297.2008.02208.xDiebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. doi: 10.1111/j.1468-0297.2008.02208.xDiebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. doi: 10.1016/j.ijforecast.2011.02.006Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. doi: 10.1016/j.ijforecast.2011.02.006Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. doi: 10.1016/j.jeconom.2014.04.012Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. doi: 10.1016/j.jeconom.2014.04.012Félix, J. A., Fernández-Pérez, A., & Sosvilla-Rivero, S. (2020). Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities. Journal of International Financial Markets Institutions and Money, 67, 101219. doi: 10.1016/j.intfin.2020.101219Félix, J. A., Fernández-Pérez, A., & Sosvilla-Rivero, S. (2020). Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities. Journal of International Financial Markets Institutions and Money, 67, 101219. doi: 10.1016/j.intfin.2020.101219Goodell, J. W., & Goutte, S. (2021). Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 101625. doi: 10.1016/j.frl.2020.101625Goodell, J. W., & Goutte, S. (2021). Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 101625. doi: 10.1016/j.frl.2020.101625Ji, Q., Bouri, E., Lau, C. K. M., & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257–272. doi: 10.1016/j.irfa.2018.12.002Ji, Q., Bouri, E., Lau, C. K. M., & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257–272. doi: 10.1016/j.irfa.2018.12.002Kammoun, W. M. (2026). Return and volatility spillover drivers among conventional cryptocurrencies. Digital Finance, 8(1). doi: 10.1007/s42521-025-00167-yKammoun, W. M. (2026). Return and volatility spillover drivers among conventional cryptocurrencies. Digital Finance, 8(1). doi: 10.1007/s42521-025-00167-yKatsiampa, P. (2019). Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30, 221–227. doi: 10.1016/j.frl.2018.10.005Katsiampa, P. (2019). Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30, 221–227. doi: 10.1016/j.frl.2018.10.005Kurka, J. (2019). Do cryptocurrencies and traditional asset classes influence each other? Finance Research Letters, 31, 38–46. doi: 10.1016/j.frl.2019.04.018Kurka, J. (2019). Do cryptocurrencies and traditional asset classes influence each other? Finance Research Letters, 31, 38–46. doi: 10.1016/j.frl.2019.04.018Mensi, W., Belghouthi, H. E., Kharusi, S. A., & Kang, S. H. (2025). Tail risk contagion and connectedness between clean cryptocurrency, green assets and commodity markets. International Review of Financial Analysis, 105, 104370. doi: 10.1016/j.irfa.2025.104370Mensi, W., Belghouthi, H. E., Kharusi, S. A., & Kang, S. H. (2025). Tail risk contagion and connectedness between clean cryptocurrency, green assets and commodity markets. International Review of Financial Analysis, 105, 104370. doi: 10.1016/j.irfa.2025.104370Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. doi: 10.1016/S0165-1765(97)00214-0Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. doi: 10.1016/S0165-1765(97)00214-0Polat, O. (2023). Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(1), 29. doi: 10.18037/ausbd.1272534Polat, O. (2023). Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(1), 29. doi: 10.18037/ausbd.1272534Rehman, S. U., Ahmad, T., Desheng, W. D., & Karamoozian, A. (2024). Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches. arXiv (Cornell University). doi: 10.48550/arxiv.2407.15766Rehman, S. U., Ahmad, T., Desheng, W. D., & Karamoozian, A. (2024). Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches. arXiv (Cornell University). doi: 10.48550/arxiv.2407.15766Wu, M., Wang, L., & Yang, H. (2024). Heterogeneity in the volatility spillover of cryptocurrencies and exchanges. Financial Innovation, 10(1), 85. doi: 10.1186/s40854-023-00585-0Wu, M., Wang, L., & Yang, H. (2024). Heterogeneity in the volatility spillover of cryptocurrencies and exchanges. Financial Innovation, 10(1), 85. doi: 10.1186/s40854-023-00585-0

The authors declare that there are no conflicts of interest present.