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A Statistical Risk Assessment Of Bitcoin And Its Extreme Tail Behavior

Author

Listed:
  • JOERG OSTERRIEDER

    () (School of Engineering, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland)

  • JULIAN LORENZ

    () (Independent Researcher, Switzerland)

Abstract

We provide an extreme value analysis of the returns of Bitcoin. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate extreme value analysis. Those properties will be compared to the traditional exchange rates of the G10 currencies versus the US dollar. For investors, especially institutional ones, an understanding of the risk characteristics is of utmost importance. So for Bitcoin to become a mainstream investable asset class, studying these properties is necessary. Our findings show that the bitcoin return distribution not only exhibits higher volatility than traditional G10 currencies, but also stronger non-normal characteristics and heavier tails. This has implications for risk management, financial engineering (such as bitcoin derivatives) — both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behavior of the cryptocurrency Bitcoin.

Suggested Citation

  • Joerg Osterrieder & Julian Lorenz, 2017. "A Statistical Risk Assessment Of Bitcoin And Its Extreme Tail Behavior," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-19, March.
  • Handle: RePEc:wsi:afexxx:v:12:y:2017:i:01:n:s2010495217500038
    DOI: 10.1142/S2010495217500038
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    References listed on IDEAS

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    1. G. William Schwert, 2011. "Stock Volatility During the Recent Financial Crisis," NBER Working Papers 16976, National Bureau of Economic Research, Inc.
    2. Coppes, R. C., 1995. "Are exchange rate changes normally distributed?," Economics Letters, Elsevier, vol. 47(2), pages 117-121, February.
    3. Kristoufek, Ladislav, 2014. "What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis," FinMaP-Working Papers 23, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    4. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    5. Christopher A. T. Ferro & Johan Segers, 2003. "Inference for clusters of extreme values," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 545-556.
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    Cited by:

    1. repec:eee:ecolet:v:164:y:2018:i:c:p:109-111 is not listed on IDEAS
    2. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar & H. Eugene Stanley & Boris Podobnik, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Papers 1803.08405, arXiv.org.

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