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The effects of the introduction of Bitcoin futures on the volatility of Bitcoin returns

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  • Kim, Wonse
  • Lee, Junseok
  • Kang, Kyungwon

Abstract

This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from five cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.

Suggested Citation

  • Kim, Wonse & Lee, Junseok & Kang, Kyungwon, 2020. "The effects of the introduction of Bitcoin futures on the volatility of Bitcoin returns," Finance Research Letters, Elsevier, vol. 33(C).
    • Handle: RePEc:eee:finlet:v:33:y:2020:i:c:s154461231830713x
    DOI: 10.1016/j.frl.2019.06.002
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    References listed on IDEAS

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    1. Galina Hale & Arvind Krishnamurthy & Marianna Kudlyak & Patrick Shultz, 2018. "How Futures Trading Changed Bitcoin Prices," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    2. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    3. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    4. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    5. Sensoy, Ahmet, 2019. "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, vol. 28(C), pages 68-73.
    6. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    7. Sang Bin Lee & Ki Yool Ohk, 1992. "Stock index futures listing and structural change in time‐varying volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 12(5), pages 493-509, October.
    8. Harald Hau, 2001. "Location Matters: An Examination of Trading Profits," Journal of Finance, American Finance Association, vol. 56(5), pages 1959-1983, October.
    9. Wonse Kim & Sungjae Jun, 2018. "Effects of a Price limit Change on Market Stability at the Intraday Horizon in the Korean Stock Market," Papers 1805.04728, arXiv.org.
    10. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    11. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    12. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    13. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    14. Corbet, Shaen & Lucey, Brian & Peat, Maurice & Vigne, Samuel, 2018. "Bitcoin Futures—What use are they?," Economics Letters, Elsevier, vol. 172(C), pages 23-27.
    15. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
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    Cited by:

    1. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Smales, L.A., 2022. "Investor attention in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. Dirk G. Baur & Lee A. Smales, 2022. "Trading behavior in bitcoin futures: Following the “smart money”," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1304-1323, July.
    4. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    5. Arun Narayanasamy & Humnath Panta & Rohit Agarwal, 2023. "Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment," JRFM, MDPI, vol. 16(11), pages 1-24, November.
    6. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2021. "Trading activity and price discovery in Bitcoin futures markets," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 107-120.
    7. Kao, Yu-Sheng & Zhao, Kai & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2024. "The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 524-542.
    8. Zhang, Chuanhai & Chen, Haicui & Peng, Zhe, 2022. "Does Bitcoin futures trading reduce the normal and jump volatility in the spot market? Evidence from GARCH-jump models," Finance Research Letters, Elsevier, vol. 47(PB).
    9. Zhang, Chuanhai & Ma, Huan & Arkorful, Gideon Bruce & Peng, Zhe, 2023. "The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Zhang, Chuanhai & Ma, Huan & Liao, Xiaosai, 2023. "Futures trading activity and the jump risk of spot market: Evidence from the bitcoin market," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    11. Weige Huang & Xiang Gao, 2023. "Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies," SAGE Open, , vol. 13(1), pages 21582440231, January.
    12. Hattori, Takahiro & Ishida, Ryo, 2021. "Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).

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    More about this item

    Keywords

    Bitcoin; Bitcoin futures; Intraday volatility; Realized volatility; Discrete Fourier transform;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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