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Risk management of Bitcoin futures with GARCH models

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  • Guo, Zi-Yi

Abstract

In this article, we investigate the quantitative risk management of Bitcoin futures by using the GARCH models. We first found that it is crucial to introduce a heavy-tailed distribution into the GARCH models to explain return volatilities of Bitcoin futures. Then, we compare the VaR estimates based on the parametric methods, namely the GARCH model with the normal distribution (GARCH-Normal) and the GARCH model with the normal inverse Gaussian distribution (GARCH-NIG), and the nonparametric method. Our results illustrate that although the VaR estimates based on the nonparametric method are overall accurate and even more accurate than the VaR estimates based on the GARCH-Normal model, the VaR estimates based on the GARCH-NIG model perform the best. Overall, we conclude that the GARCH-NIG model could generate accurate VaR estimates for the Bitcoin futures return series. In addition, we found that in contrast to Bitcoin cash, the return volatilities of the Bitcoin futures do not increase by more in response to positive shocks than in response to negative shocks.

Suggested Citation

  • Guo, Zi-Yi, 2022. "Risk management of Bitcoin futures with GARCH models," Finance Research Letters, Elsevier, vol. 45(C).
    • Handle: RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002671
    DOI: 10.1016/j.frl.2021.102197
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    1. Wu, Ping-Tsung & Shieh, Shwu-Jane, 2007. "Value-at-Risk analysis for long-term interest rate futures: Fat-tail and long memory in return innovations," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 248-259, March.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Carol Alexander & Jaehyuk Choi & Heungju Park & Sungbin Sohn, 2020. "BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 23-43, January.
    6. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    7. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    8. Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
    9. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    10. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    11. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    12. 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.
    13. Guo, Zi-Yi, 2021. "Price volatilities of bitcoin futures," Finance Research Letters, Elsevier, vol. 43(C).
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    16. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    17. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836, Decembrie.
    18. Morten B. Jensen & Asger Lunde, 2001. "The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model," Econometrics Journal, Royal Economic Society, vol. 4(2), pages 1-10.
    19. Dwita Mariana, Christy & Ekaputra, Irwan Adi & Husodo, Zaäfri Ananto, 2021. "Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic?," Finance Research Letters, Elsevier, vol. 38(C).
    20. Niels S. GrØnborg & Asger Lunde, 2016. "Analyzing Oil Futures with a Dynamic Nelson‐Siegel Model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(2), pages 153-173, February.
    21. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    22. Takahiro Hattori & Ryo Ishida, 2021. "The relationship between arbitrage in futures and spot markets and Bitcoin price movements: Evidence from the Bitcoin markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 105-114, January.
    23. Andersson, Jonas, 2001. "On the Normal Inverse Gaussian Stochastic Volatility Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 44-54, January.
    24. Sebastião, Helder & Godinho, Pedro, 2020. "Bitcoin futures: An effective tool for hedging cryptocurrencies," Finance Research Letters, Elsevier, vol. 33(C).
    25. Carlos Trucíos & Aviral K. Tiwari & Faisal Alqahtani, 2020. "Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach," Applied Economics, Taylor & Francis Journals, vol. 52(24), pages 2580-2593, May.
    26. Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019. "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, vol. 30(C), pages 187-193.
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    Cited by:

    1. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.

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    More about this item

    Keywords

    Bitcoin; Value-at-risk; Heavy-tailed distribution;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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