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Cryptocurrency volatility markets

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  • Fabian Woebbeking

    (Goethe University Frankfurt)

Abstract

By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.

Suggested Citation

  • Fabian Woebbeking, 2021. "Cryptocurrency volatility markets," Digital Finance, Springer, vol. 3(3), pages 273-298, December.
  • Handle: RePEc:spr:digfin:v:3:y:2021:i:3:d:10.1007_s42521-021-00037-3
    DOI: 10.1007/s42521-021-00037-3
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    Cited by:

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    2. Dmitry V. Boguslavsky & Natalia P. Sharova & Konstantin S. Sharov, 2021. "Cryptocurrency as Epidemiologically Safe Means of Transactions: Diminishing Risk of SARS-CoV-2 Spread," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    3. Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023. "Deep State-Space Model for Predicting Cryptocurrency Price," Papers 2311.14731, arXiv.org.
    4. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    5. Boyi Li & Weixuan Xia, 2024. "Crypto Inverse-Power Options and Fractional Stochastic Volatility," Papers 2403.16006, arXiv.org, revised Sep 2024.
    6. Walid Mensi & Mariya Gubareva & Hee-Un Ko & Xuan Vinh Vo & Sang Hoon Kang, 2023. "Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    7. Goel, Rajeev K. & Mazhar, Ummad, 2024. "Cryptocurrency use and tax collections: Direct and indirect channels of influence," Journal of Financial Stability, Elsevier, vol. 72(C).
    8. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.

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    More about this item

    Keywords

    Cryptocurrency; Blockchain; Bitcoin; Volatility; Derivatives; Options; Liquidity;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services

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