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Dependence between bitcoin and African currencies

Author

Listed:
  • Saralees Nadarajah

    (University of Manchester)

  • Emmanuel Afuecheta

    (King Fahd University of Petroleum and Minerals)

  • Stephen Chan

    (American University of Sharjah)

Abstract

Africa being one of the poorest continents is an ideal platform for the use of bitcoin. Considering the specific nature of the continent, is this digital currency going to be a propelling factor for its growth? In this paper, we investigate the correlation and dependence structure between bitcoin and eight African currencies using two different concepts, including extreme correlation plots and a bivariate extreme value model due to Gumbel (Bull Inst Int de Stat 37:471475, 1960). The currencies are chosen to correspond to eight biggest economies in Africa. We identify those African currencies having a positive correlation with bitcoin and those having a negative correlation with bitcoin. The dependence quantified can have economic implications. We perform a robustness study to see if the results hold also for parts of the data. We provide economic interpretations of the results which could be of interest to researchers, policy makers and bitcoin investors within the region and beyond.

Suggested Citation

  • Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2021. "Dependence between bitcoin and African currencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1203-1218, August.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:4:d:10.1007_s11135-020-01051-0
    DOI: 10.1007/s11135-020-01051-0
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    References listed on IDEAS

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    1. Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
    2. Lunde A. & Timmermann A., 2004. "Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 253-273, July.
    3. Ralph C. Maloumby-Baka & Christian Kingombe, 2015. "The Quest to Lower High Remittance Costs to Africa: A Brief Review of the Use of Mobile Banking and Bitcoins," CFD Working Papers 10-2015, Centre for Finance and Development, The Graduate Institute.
    4. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    5. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
    6. Raphael Warren Jankeeparsad & Dev Tewari, 2018. "End-User Adoption of Bitcoin in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 10(5), pages 230-243.
    7. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
    8. Gkillas, Konstantinos & Longin, François, 2018. "Financial market activity under capital controls: Lessons from extreme events," Economics Letters, Elsevier, vol. 171(C), pages 10-13.
    9. Zhang, Yuanyuan & Chu, Jeffrey & Chan, Stephen & Chan, Brandon, 2019. "The generalised hyperbolic distribution and its subclass in the analysis of a new era of cryptocurrencies: Ethereum and its financial risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    10. Pisso Nseke, 2018. "How Crypto-Currency Can Decrypt the Global Digital Divide: Bitcoins a Means for African Emergence," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 3(6), pages 61-70, February.
    11. Michel Rauchs & Garrick Hileman, 2017. "Global Cryptocurrency Benchmarking Study," Cambridge Centre for Alternative Finance Reports 201704-gcbs, Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
    12. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    13. Zhang, Yuanyuan & Chan, Stephen & Chu, Jeffrey & Nadarajah, Saralees, 2019. "Stylised facts for high frequency cryptocurrency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 598-612.
    14. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
    15. Longin, François & Pagliardi, Giovanni, 2016. "Tail relation between return and volume in the US stock market: An analysis based on extreme value theory," Economics Letters, Elsevier, vol. 145(C), pages 252-254.
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