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Could this be a fiction? Bitcoin forecasts most tradable currency pairs better than ARFIMA

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometric and Allied Research, University of Ibadan)

  • Lateef O. Akanni

    (Department of Economics, University of Lagos,Akoka, Lagos, Nigeria)

  • Rasheed O. Azeez

    (Department of Economics, University of Ibadan, Nigeria.
    Department of Economics, Fountain University, Nigeria.)

Abstract

In this paper, we attempt to exploit any inherent useful information in Bitcoin to predict the future path of the most tradable currency pairs in the world. We also verify whether the forecast outcomes can compare favourably with the time series model such as the fractionally integrated autoregressive moving average (ARFIMA) model. We follow the Lewellen (2004) and Westerlund and Narayan (2102, 2015) approaches that account for any statistical effect that could bias the regression estimates. Our results suggest that Bitcoin is a good predictor of the selected currency pairs and more importantly, its forecast results outperform the time series model judging by the Diebold and Mariano test regardless of the data sample and forecast horizon. Although, recent evidence in the literature seems to suggest that the Bitcoin bubble will soon burst, its connection with the considered currency pairs may be exploited while it lasts.

Suggested Citation

  • Afees A. Salisu & Lateef O. Akanni & Rasheed O. Azeez, 2018. "Could this be a fiction? Bitcoin forecasts most tradable currency pairs better than ARFIMA," Working Papers 051, Centre for Econometric and Allied Research, University of Ibadan.
    • Handle: RePEc:cui:wpaper:0051
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    References listed on IDEAS

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    1. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    2. Makin, Anthony J. & Narayan, Paresh Kumar & Narayan, Seema, 2014. "What expenditure does Anglosphere foreign borrowing fund?," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 63-78.
    3. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    4. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    5. Elie Bouri & Mahamitra Das & Rangan Gupta & David Roubaud, 2018. "Spillovers between Bitcoin and other assets during bear and bull markets," Applied Economics, Taylor & Francis Journals, vol. 50(55), pages 5935-5949, November.
    6. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    7. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    8. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    9. Baur, Dirk G. & Dimpfl, Thomas & Kuck, Konstantin, 2018. "Bitcoin, gold and the US dollar – A replication and extension," Finance Research Letters, Elsevier, vol. 25(C), pages 103-110.
    10. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    11. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    12. Deepa & Paresh K Narayan, "undated". "Are Indian Stock Returns Predictable?," Working Papers 2015_07, Deakin University, Department of Economics.
    13. Sharma, Susan Sunila, 2016. "Can consumer price index predict gold price returns?," Economic Modelling, Elsevier, vol. 55(C), pages 269-278.
    14. Wali, Muammer & Chan, Felix & Manzur, Meher, 2017. "Nonlinear dependence in exchange rate returns: How do emerging Asian currencies compare with major currencies?," Journal of Asian Economics, Elsevier, vol. 50(C), pages 62-72.
    15. 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.
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    Cited by:

    1. Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

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    More about this item

    Keywords

    Bitcoin; Exchange rates; Forecast evaluation;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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