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Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks

Author

Listed:
  • Amélie Charles

    (Audencia Recherche - Audencia Business School)

  • Olivier Darné

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes)

Abstract

In this paper we study the daily volatility of four cryptocurrencies (BitCoin, Dash, LiteCoin, and Ripple) from June 2014 to November 2018. We first show that the cryptocurrency returns are strongly characterized by the presence of jumps as well as structural breaks (except Dash). Then, we estimate four GARCH-type models that capture short memory (GARCH), asymmetry (APARCH), strong persistence (IGARCH), and long memory (FIGARCH) from (i) original returns, (ii) jump-filtered returns, and (iii) jump-filtered returns with structural breaks. Results indicate the importance to take into account the jumps and structural breaks in modelling volatility of the cryptocurrencies. It appears that the cryptocurrency returns are well modelled by infinite persistence (BitCoin, Dash, and LiteCoin) or long memory (Ripple) with a Student-t distribution.

Suggested Citation

  • Amélie Charles & Olivier Darné, 2019. "Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks," Post-Print hal-03794543, HAL.
    • Handle: RePEc:hal:journl:hal-03794543
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    References listed on IDEAS

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    Cited by:

    1. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Robert Mudida & Nuruddeen Abu, 2021. "Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1318-1335, January.
    2. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    3. Walid Chkili, 2021. "Modeling Bitcoin price volatility: long memory vs Markov switching," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 433-448, September.
    4. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    5. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    6. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    7. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.

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    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • G1 - Financial Economics - - General Financial Markets

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