IDEAS home Printed from https://ideas.repec.org/p/mcd/mcddps/2025_02.html

Tracing Contagion between Bitcoin and Traditional Markets

Author

Listed:
  • Grigorios Rapos

    (Department of Economics, University of Macedonia)

  • Stylianos Fountas

    (Department of Economics, University of Macedonia)

Abstract

This study investigates the presence of contagion between Bitcoin and four traditional assets (stocks, bonds, gold, and the U.S. dollar exchange rate) over the period 2015-2024. By implementing a framework that combines the DCC-GARCH specification and a time-varying causal inference methodology, we investigate periods of financial contagion. Overall, our findings support that Bitcoin remains weakly connected to the global financial markets. Our main results are as follows: First, among all assets, Bitcoin significantly Granger causes only the U.S. dollar exchange rate during the post-2021 sample period. Second, Bitcoin is Granger caused by the stock market and the US dollar exchange rate. Third, there is evidence for rare contagion episodes running from Bitcoin to the U.S. dollar exchange rate, while there is no evidence of contagion from Bitcoin to all other assets (stocks, bonds, and gold). Fourth, sporadic contagion also applies from the stock market to Bitcoin, mostly in May 2022. Our evidence implies that Bitcoin may be used as a useful portfolio diversification instrument.

Suggested Citation

  • Grigorios Rapos & Stylianos Fountas, 2025. "Tracing Contagion between Bitcoin and Traditional Markets," Discussion Paper Series 2025_02, Department of Economics, University of Macedonia, revised Feb 2025.
    • Handle: RePEc:mcd:mcddps:2025_02
    as

    Download full text from publisher

    File URL: http://econwp.uom.gr/pdf/dp022025.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    2. Bodart, Vincent & Candelon, Bertrand, 2009. "Evidence of interdependence and contagion using a frequency domain framework," Emerging Markets Review, Elsevier, vol. 10(2), pages 140-150, June.
    3. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    4. Abid, Ilyes & Bouri, Elie & Galariotis, Emilios & Guesmi, Khaled & Mzoughi, Hela, 2023. "Bitcoin vs. fiat currencies: Insights from extreme dependence and risk spillover analysis with financial markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. Dario Caldara & Sarah Conlisk & Matteo Iacoviello & Maddie Penn, 2022. "The Effect of the War in Ukraine on Global Activity and Inflation," FEDS Notes 2022-05-27-2, Board of Governors of the Federal Reserve System (U.S.).
    6. Atasoy, Burak Sencer & Özkan, İbrahim, 2024. "Correlation meets causality: A holistic measure of financial contagion," Finance Research Letters, Elsevier, vol. 65(C).
    7. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bouteska, Ahmed & Hassan, M. Kabir & Rashid, Mamunur & Bilgin, Mehmet Hüseyin, 2024. "The dynamics of bonds, commodities and bitcoin based on NARDL approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 58-70.
    2. Bouri, Elie & Lucey, Brian & Roubaud, David, 2020. "Cryptocurrencies and the downside risk in equity investments," Finance Research Letters, Elsevier, vol. 33(C).
    3. Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    4. Weisskopf, Jean-Philippe, 2020. "Breaking bad: An investment in cannabis," Finance Research Letters, Elsevier, vol. 33(C).
    5. Hampl, Filip & Vágnerová Linnertová, Dagmar & Horváth, Matúš, 2024. "Crypto havens during war times? Evidence from the Russian invasion of Ukraine," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    6. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    7. Zhang, Chuanhai & Ma, Huan & Arkorful, Gideon Bruce & Peng, Zhe, 2023. "The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    8. Wang, Wei & Enilov, Martin & Stankov, Petar, 2025. "Can cryptocurrency or gold rescue BRICS stocks amid the Russia-Ukraine conflict?," International Review of Financial Analysis, Elsevier, vol. 104(PA).
    9. Shaen Corbet & Yang Hou & Yang Hu & Les Oxley, 2024. "Time varying risk aversion and its connectedness: evidence from cryptocurrencies," Annals of Operations Research, Springer, vol. 338(2), pages 879-923, July.
    10. Cevik, Emrah Ismail & Gunay, Samet & Dibooglu, Sel & Yıldırım, Durmuş Çağrı, 2023. "The impact of expected and unexpected events on Bitcoin price development: Introduction of futures market and COVID-19," Finance Research Letters, Elsevier, vol. 54(C).
    11. Kreuzer, Christian & Laschinger, Ralf & Priberny, Christopher & Benninghoff, Sven, 2024. "Cryptocurrencies as a vehicle for capital exodus: Evidence from the Russian–Ukrainian crisis," Finance Research Letters, Elsevier, vol. 69(PB).
    12. Baur, Dirk G. & Hoang, Lai T., 2021. "A crypto safe haven against Bitcoin," Finance Research Letters, Elsevier, vol. 38(C).
    13. P. S. Niveditha, 2025. "Identifying Safe Haven Assets: Evidence from Fractal Market Hypothesis," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 313-335, January.
    14. Ángeles Cebrián-Hernández & Enrique Jiménez-Rodríguez, 2021. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    15. Meng-Shiuh Chang & Meng-Wei Chen & Peijie Ju, 2023. "Asymmetry in Hedges, Safe Havens, Flights and Contagion: Unconditional Quantile Regression Approach," SAGE Open, , vol. 13(4), pages 21582440231, November.
    16. Baur, Dirk G. & Karlsen, Jonathan R. & Smales, Lee A. & Trench, Allan, 2024. "Digging deeper - Is bitcoin digital gold? A mining perspective," Journal of Commodity Markets, Elsevier, vol. 34(C).
    17. Daeyun Kang & Doojin Ryu & Robert I. Webb, 2025. "Bitcoin as a financial asset: a survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-28, December.
    18. Beneki, Christina & Koulis, Alexandros & Kyriazis, Nikolaos A. & Papadamou, Stephanos, 2019. "Investigating volatility transmission and hedging properties between Bitcoin and Ethereum," Research in International Business and Finance, Elsevier, vol. 48(C), pages 219-227.
    19. Urquhart, Andrew & Zhang, Hanxiong, 2019. "Is Bitcoin a hedge or safe haven for currencies? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 49-57.
    20. Urszula Gieraltowska & Andrzej Rzeczycki, 2025. "Can Bitcoin Replace Gold in an Investment Portfolio in the Polish Capital Market?," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 1587-1608.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mcd:mcddps:2025_02. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Theodore Panagiotidis or Anastasia Litina (email available below). General contact details of provider: http://www.uom.gr/index.php?tmima=3 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.