THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE

Elif Kaya; Zahra Ahmadi

doi:10.33818/ier.1804057

EN

THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE

Abstract

The paper examines the volatility spillover effects and long-term relationship between cryptocurrencies and traditional financial markets in Türkiye using BEKK-GARCH and DCC-GARCH models. It analyses the perception of crypto assets as a “digital safe haven” in an economy marked by high inflation, exchange rate fragility, and financial uncertainty. Using monthly price data for Bitcoin, Ethereum, BIST-100, and Republic Gold from January 2010 to February 2025, the study applies unit root tests, Johansen cointegration, ARDL bounds, and Engle-Granger tests. Results show no long-term price cointegration, but Bitcoin and Ethereum returns are strongly correlated, with DCC-GARCH results showing a dynamic correlation above 50%, while gold and BIST-100 correlate weakly or negatively. BEKK-GARCH highlights significant volatility transmission from Bitcoin to Ethereum, with BIST-100 maintaining persistent volatility. The study concludes that crypto and traditional markets in Türkiye are not integrated long-term, but short-term interactions exist at the return level, with implications for portfolio diversification and financial stability.

Keywords

Ethical Statement

Regarding the manuscript entitled "The Spillover Effects and Long-Term Relationship Between Cryptocurrencies and Traditional Financial Markets in Türkiye," the authors hereby declare that: Authorship: All persons listed as authors have made substantial intellectual contributions to the manuscript. All authors have read and approved the final version of the paper and agree to be accountable for all aspects of the work. Originality and Plagiarism: The manuscript is an original work of the authors and has not been published previously, in whole or in part, nor is it under consideration for publication elsewhere. All works and ideas of others have been properly cited and referenced. Data Access and Integrity: The data used in this research (Bitcoin, Ethereum, BIST-100, and Republic Gold prices from January 2010 to February 2025) were obtained from publicly available sources. The data collection and analysis processes were conducted with rigor and transparency to ensure the integrity of the research findings. Conflict of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper, whether financial, personal, or professional, that could have influenced the research or its outcomes. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical Conduct of Research: As this study is based exclusively on publicly available financial market data and does not involve any direct human or animal subjects, formal approval from an institutional ethics committee was not required.

Thanks

We extend our special thanks to Dr. Mortaza Ojaghlou for his expert guidance and thoughtful comments, which greatly improved the quality of this research.

References

Bahmani-Oskooee, M. and M. T. Bohl (2000). German monetary unification and the stability of the German M3 money demand function. Economics Letters, 66(2), 203–208.
Baur, D. G. and B. M. Lucey (2009). Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold.
Chen, T., Li, Z., Y. Zhu, j. Chen, X. Luo, J. C.-S Lui, X. Lin and X. Zhang (2020). Understanding Ethereum via Graph Analysis. ACM Transactions on Internet Technology, 20(2), 1–32.
Demmler, M. (2023). Leading Cryptocurrencies Between 2019 and 2021: Analysis of Market Prices and Portfolio Design. Análisis Económico, 38(99), 145–165.
Dickey, D. A. and W. A. Fuller (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root STOR.
Diebold, F. X. and K. Yilmaz (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66.
Engle, R. F. (2000). Dynamic Conditional Correlation—A Simple Class of Multivariate GARCH Models. SSRN Electronic Journal.
Engle, R. F. and K. F. Kroner (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11(1), 122–150.

Engle, R. and K. Sheppard (2001). Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH (No. W8554; p. w8554). National Bureau of Economic Research.
Engle, Robert F. and C. W. J. Granger (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251–276.
Hamilton, J. D. (2008). Macroeconomics and ARCH (NBER WORKING PAPER SERIES). (NO.14151).
Hanif, W., H.-U. Ko, L. Pham and S. H. Kang (2023). Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets. Financial Innovation, 9(1), 84.
Huynh, T. L. D., M. A. Nasir, X. V. Vo. and T. T. Nguyen (2020). “Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet. The North American Journal of Economics and Finance, 54, 101277.
Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551.
Karampinakis, E., M. Pachilakis, P. Papadopoulos, A. Krithinakis and E. P. Markatos (2019). The coin that never sleeps. The privacy preserving usage of Bitcoin in a longitudinal analysis as a speculative asset (arXiv:1911.03226). arXiv.
Keshari, A. and A. Gautam (2023). Stock Market Volatility Due to Cross-Listing of Tradable Assets. Indian Journal of Finance, 17(9), 58.
Li, Y. (2024). Blockchain and Cryptocurrency: The Future of Financial Innovation and Challenges. Advances in Economics, Management and Political Sciences, 88(1), 137–143.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Mensi, W., M. Gubareva, H. U. Ko, X. V. Vo and S. H. Kang (2023). Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets. Financial Innovation, 9(1), 92.
Ojaghlou, M. and Ö. Demi̇rkale (2023). Bitcoin ile borsa endeksleri ve seçili finansal varliklar arasindaki uzun dönem asimetrik ilişkisi: kirilgan beşli ülkeler örneği. Abant Sosyal Bilimler Dergisi, 23(2), 1046–1065.
Oksanen, A., H. Hagfors., I. Vuorinen and I. Savolainen (2022). Longitudinal perspective on cryptocurrency trading and increased gambling problems: A 3-wave national survey study. Public Health, 213, 85–90.
Pesaran, M. H., Y. Shin and R. J. Smith (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.
Pesaran, M. H., Y. Shin and R. P. Smith (1999). Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, 94(446), 621–634.
Phillips, P. C. B. and P. Perron (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 334-346.
Rose, C. (2015). The Evolution of Digital Currencies: Bitcoin, A Cryptocurrency Causing A Monetary Revolution. International Business and Economics Research Journal (IBER), 14(4), 617.
Tikhomirov, S. (2018). Ethereum: State of Knowledge and Research Perspectives. In A. Imine, J. M. Fernandez, J.-Y. Marion, L. Logrippo, and J. Garcia-Alfaro (Eds.), Foundations and Practice of Security (Vol. 10723, pp. 206–221). Springer International Publishing.
Umar, Z., O. Polat, S. Y. Choi and T. Teplova. (2022). Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework. Pacific-Basin Finance Journal, 76, 101876.
Vuković, D. B., M. Frömmel, S. A. Vigne and V. Zinovev (2025). Spillovers between cryptocurrencies and financial markets in a global framework. Journal of International Money and Finance, 150, 103235.
Wang, Q. (2024). Research on the Impact of Digital Currency on the Operation Mechanism of Traditional Financial Markets and Countermeasures. Applied Mathematics and Nonlinear Sciences, 9(1), 20242887.
Zha, R., L. Yu, Y. Su and H. Yin (2023). Dependences and risk spillover effects between Bitcoin, crude oil and other traditional financial markets during the COVID-19 outbreak. Environmental Science and Pollution Research, 30(14), 40737–40751.

Details

Primary Language

English

Subjects

Time-Series Analysis

Journal Section

Research Article

Authors

Elif Kaya ^*
0000-0002-9757-5155
Türkiye

Zahra Ahmadi
0009-0003-8283-3283
Türkiye

Publication Date

June 1, 2026

Submission Date

October 15, 2025

Acceptance Date

May 25, 2026

Published in Issue

Year 2026 Volume: 18 Number: 1

DOI

https://doi.org/10.33818/ier.1804057

IZ

https://izlik.org/JA54JB94HB

Cite

RIS / Bibtex

APA

Kaya, E., & Ahmadi, Z. (2026). THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE. International Econometric Review, 18(1), 1-23. https://doi.org/10.33818/ier.1804057

AMA

1.Kaya E, Ahmadi Z. THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE. IER. 2026;18(1):1-23. doi:10.33818/ier.1804057

Chicago

Kaya, Elif, and Zahra Ahmadi. 2026. “THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE”. International Econometric Review 18 (1): 1-23. https://doi.org/10.33818/ier.1804057.

EndNote

Kaya E, Ahmadi Z (June 1, 2026) THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE. International Econometric Review 18 1 1–23.

IEEE

[1]E. Kaya and Z. Ahmadi, “THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE”, IER, vol. 18, no. 1, pp. 1–23, June 2026, doi: 10.33818/ier.1804057.

ISNAD

Kaya, Elif - Ahmadi, Zahra. “THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE”. International Econometric Review 18/1 (June 1, 2026): 1-23. https://doi.org/10.33818/ier.1804057.

JAMA

1.Kaya E, Ahmadi Z. THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE. IER. 2026;18:1–23.

MLA

Kaya, Elif, and Zahra Ahmadi. “THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE”. International Econometric Review, vol. 18, no. 1, June 2026, pp. 1-23, doi:10.33818/ier.1804057.

Vancouver

1.Elif Kaya, Zahra Ahmadi. THE SPILLOVER EFFECTS AND LONG-TERM RELATIONSHIP BETWEEN CRYPTOCURRENCIES AND TRADITIONAL FINANCIAL MARKETS IN TÜRKİYE. IER. 2026 Jun. 1;18(1):1-23. doi:10.33818/ier.1804057