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Causality between stock indices and cryptocurrencies before and during the Russo–Ukrainian war

Author

Listed:
  • Nidhal Mgadmi

    (University of Manouba-Tunisia)

  • Tarek Sadraoui

    (University of Manouba-Tunisia)

  • Ameni Abidi

    (University of Manouba-Tunisia)

Abstract

In this paper, we study the unidirectional interdependence between the stock indices of Japan, Canada, Germany, France, Russia, Ukraine, and the United States with the seven most popular cryptocurrencies: Bitcoin, Ethereum, Litecoin, Dash, Ripple, DigiByte, and XEM. We focus on two sub-periods: the first before the war in July 1, 2017 to September 31, 2019 and the second during the war in the start of the Russian invasion of Ukraine to December 16, 2023. We use the Granger causality test (Econom J Econom Soc 37:424–438, 1969) in levels to investigate the strong dependence between the seven stock indices and the seven cryptocurrencies. We find that the unidirectional dependence between these two asset classes disappears during the war due to high volatility, as evidenced by the GARCH and FGARCH models.

Suggested Citation

  • Nidhal Mgadmi & Tarek Sadraoui & Ameni Abidi, 2024. "Causality between stock indices and cryptocurrencies before and during the Russo–Ukrainian war," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 71(2), pages 301-323, June.
    • Handle: RePEc:spr:inrvec:v:71:y:2024:i:2:d:10.1007_s12232-023-00444-5
    DOI: 10.1007/s12232-023-00444-5
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    as
    1. Maghyereh, Aktham & Abdoh, Hussein, 2020. "Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach," International Review of Financial Analysis, Elsevier, vol. 71(C).
    2. Kamal, Md Rajib & Ahmed, Shaker & Hasan, Mostafa Monzur, 2023. "The impact of the Russia-Ukraine crisis on the stock market: Evidence from Australia," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    3. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2016. "What drives Bitcoin price?," Economics Bulletin, AccessEcon, vol. 36(2), pages 843-850.
    4. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CIRJE F-Series CIRJE-F-1078, CIRJE, Faculty of Economics, University of Tokyo.
    5. Anders Stensås & Magnus Frostholm Nygaard & Khine Kyaw & Sirimon Treepongkaruna, 2019. "Can Bitcoin be a diversifier, hedge or safe haven tool?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1593072-159, January.
    6. Muhammad Ali Nasir & Min Du, 2018. "Integration of Financial Markets in Post Global Financial Crises and Implications for British Financial Sector: Analysis Based on A Panel VAR Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 363-388, June.
    7. 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.
    8. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Boubaker, Sabri & Goodell, John W. & Pandey, Dharen Kumar & Kumari, Vineeta, 2022. "Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 48(C).
    10. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    11. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    12. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    13. Achraf Ghorbel & Wajdi Frikha & Yasmine Snene Manzli, 2022. "Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 387-425, September.
    14. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    15. Lo, Gaye-Del & Marcelin, Isaac & Bassène, Théophile & Sène, Babacar, 2022. "The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities," Finance Research Letters, Elsevier, vol. 50(C).
    16. Goodell, John W. & Yadav, Miklesh Prasad & Ruan, Junhu & Abedin, Mohammad Zoynul & Malhotra, Nidhi, 2023. "Traditional assets, digital assets and renewable energy: Investigating connectedness during COVID-19 and the Russia-Ukraine war," Finance Research Letters, Elsevier, vol. 58(PA).
    17. Khalfaoui, Rabeh & Gozgor, Giray & Goodell, John W., 2023. "Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis," Finance Research Letters, Elsevier, vol. 52(C).
    18. 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).
    19. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    20. Goodell, John W., 2020. "COVID-19 and finance: Agendas for future research," Finance Research Letters, Elsevier, vol. 35(C).
    21. Yousaf, Imran & Patel, Ritesh & Yarovaya, Larisa, 2022. "The reaction of G20+ stock markets to the Russia–Ukraine conflict “black-swan” event: Evidence from event study approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 35(C).
    22. Isaac Appiah-Otoo, 2023. "The Impact of the Russia-Ukraine War on the Cryptocurrency Market," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 4(1), pages 1-5.
    23. Wang, Pengfei & Zhang, Wei & Li, Xiao & Shen, Dehua, 2019. "Is cryptocurrency a hedge or a safe haven for international indices? A comprehensive and dynamic perspective," Finance Research Letters, Elsevier, vol. 31(C), pages 1-18.
    24. Nakano, Masafumi & Takahashi, Akihiko & Takahashi, Soichiro, 2018. "Bitcoin technical trading with artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 587-609.
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    More about this item

    Keywords

    FGARCH; Russo–Ukrainian war; Cryptocurrencies; FGARCH; Causality;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • E11 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Marxian; Sraffian; Kaleckian
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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