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Trade Uncertainties and the Hedging Abilities of Bitcoin

Author

Listed:
  • Elie Bouri

    () (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • Konstantinos Gkillas

    () (Department of Business Administration, University of Patras − University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

Abstract

In this paper, we use daily data from October 2011 to May 2019 to estimate the monthly realized correlation between stock returns of the United States (US) and Bitcoin returns. Then, we relate the realized correlation with a news-based measure of the growth of trade uncertainty for the US. Our results show that the realized correlation is negatively impacted by increases in trade uncertainty, suggesting that Bitcoin can act as a hedge relative to the US stock market in the wake of heightened trade policy-related uncertainties, and can provide diversification benefits for investors.

Suggested Citation

  • Elie Bouri & Konstantinos Gkillas & Rangan Gupta, 2019. "Trade Uncertainties and the Hedging Abilities of Bitcoin," Working Papers 201948, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201948
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    References listed on IDEAS

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    1. Fang, Libing & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019. "Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 29-36.
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    3. Schwert, G William & Seguin, Paul J, 1990. " Heteroskedasticity in Stock Returns," Journal of Finance, American Finance Association, vol. 45(4), pages 1129-1155, September.
    4. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    5. Michael McAleer, 2019. "What They Did Not Tell You about Algebraic (Non-) Existence, Mathematical (IR-)Regularity and (Non-) Asymptotic Properties of the Full BEKK Dynamic Conditional Covariance Model," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-7, April.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    7. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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    9. Barroso, Pedro & Santa-Clara, Pedro, 2015. "Momentum has its moments," Journal of Financial Economics, Elsevier, vol. 116(1), pages 111-120.
    10. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    11. Michael McAleer, 2019. "What They Did Not Tell You about Algebraic (Non-) Existence, Mathematical (IR-)Regularity, and (Non-) Asymptotic Properties of the Dynamic Conditional Correlation (DCC) Model," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-9, April.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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    More about this item

    Keywords

    US stock market; Bitcoin; realized correlation; trade uncertainty;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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