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Could stock hedge Bitcoin risk(s) and vice versa?

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  • David Iheke Okorie

    (Wang Yanan Institute for Studies in Economics (WISE), Xiamen University)

Abstract

This paper is saddled with the task of investigating the Bitcoin market behavior in the presence of a government risk. This is because both the institutional and retail investors’ interests in the Bitcoin market are growing rapidly. Conversely, the seemingly unregulated nature of this market is a serious concern to most economies and results to the placement of ban on Initial Coin Offering (ICO) in some economies by the government. Daily series of return and volume within the window of the ICO ban in China was used for the Bitcoin market and S&P500 stock market to examine the effect of a government risk in the Bitcoin market and possible hedging capabilities. Empirical results show that the ban dampened Bitcoin returns and the returns from each market can predict the other. The Exogenous Dynamic Conditional Correlation (Exo-DCC) model result suggests that, yes! the S&P500 stocks are capable of hedging Bitcoin risk, while Bitcoin can also hedge S&P500 stocks’ risks and vice versa. The Exogenous BEKK (Exo-BEKK) model result shows evidence of bidirectional volatility spill over between the two markets studied. In practice, investors (institutions and retailers) can comfortably form a robust investment portfolio with (at least) these two assets and develop a hedging strategy, such that the impacts of risks on the portfolio’s returns are safely hedged.

Suggested Citation

  • David Iheke Okorie, 2020. "Could stock hedge Bitcoin risk(s) and vice versa?," Digital Finance, Springer, vol. 2(1), pages 117-136, September.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:1:d:10.1007_s42521-019-00011-0
    DOI: 10.1007/s42521-019-00011-0
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    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
    3. 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.
    4. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Bouri, Elie & Azzi, Georges & Dyhrberg, Anne Haubo, 2017. "On the return-volatility relationship in the Bitcoin market around the price crash of 2013," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-16.
    8. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    9. Jian Yang & Yinggang Zhou & Wai Leung, 2012. "Asymmetric Correlation and Volatility Dynamics among Stock, Bond, and Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 45(2), pages 491-521, August.
    10. Anne Haubo Dyhrberg, 2015. "Hedging Capabilities of Bitcoin. Is it the virtual gold?," Working Papers 201521, School of Economics, University College Dublin.
    11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    12. Ruiping Liu & Zhichao Shao & Guodong Wei & Wei Wang, 2017. "GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns," Journal of Accounting, Business and Finance Research, Scientific Publishing Institute, vol. 1(1), pages 71-75.
    13. Ashley, Richard A. & Patterson, Douglas M., 2010. "A Test Of The Garch(1, 1) Specification For Daily Stock Returns," Macroeconomic Dynamics, Cambridge University Press, vol. 14(S1), pages 137-144, May.
    14. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    15. Capie, Forrest & Mills, Terence C. & Wood, Geoffrey, 2005. "Gold as a hedge against the dollar," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(4), pages 343-352, October.
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    Citations

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    Cited by:

    1. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    2. David I. Okorie, 2021. "A network analysis of electricity demand and the cryptocurrency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3093-3108, April.
    3. Okorie, David Iheke & Lin, Boqiang, 2023. "Cryptocurrency spectrum and 2020 pandemic: Contagion analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 29-38.
    4. Raifu, Isiaka Akande & Ogbonna, Ahamuefula E, 2021. "Safe-haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 worst-hit African Countries," MPRA Paper 113139, University Library of Munich, Germany.
    5. David Iheke Okorie & Boqiang Lin, 2022. "Crude oil market and Nigerian stocks: An asymmetric information spillover approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4002-4017, October.
    6. Ibhagui, Oyakhilome, 2021. "Stock market and deviations from covered interest parity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    7. Yongzhi Gong & Xiaofei Tang & En-Chung Chang, 2023. "Group norms and policy norms trigger different autonomous motivations for Chinese investors in cryptocurrency investment," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    8. Okorie, David Iheke & Lin, Boqiang, 2021. "Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    Risk management; Hedging; Return; Volatility; Bitcoin; Stock;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • R53 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Public Facility Location Analysis; Public Investment and Capital Stock

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