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Volatility estimation for Bitcoin: Replication and robustness

Author

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  • 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)

  • Amélie Charles

    (Audencia Business School)

Abstract
Katsiampa [Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6, 2017] compares several GARCH-type models to estimate volatility for Bitcoin returns. First, we propose a replication study (i) by verification, using the same sample and period (July 2010 to October 2016), and (ii) by reproduction, extending the sample until March 2018. We obtain only partially different results from those of Kasiampa (2017) on both samples. Second, we propose a robustness analysis (i) by reanalysis, using the robust QML estimator for computing the standard errors of the parameters, and (ii) by extension, taking into account the presence of jumps in the Bitcoin returns. The results show that the six GARCH-type models studied, namely GARCH-type models characterized by short memory, asymmetric effects, or long-run and short-run movements, seem not to be appropriate for modelling the Bitcoin returns.

Suggested Citation

  • Olivier Darné & Amélie Charles, 2019. "Volatility estimation for Bitcoin: Replication and robustness," Post-Print hal-01941102, HAL.
  • Handle: RePEc:hal:journl:hal-01941102
    DOI: 10.1016/j.inteco.2018.06.004
    Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-01941102
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    Cited by:

    1. 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).
    2. Vahidin Jeleskovic & Mirko Meloni & Zahid Irshad Younas, 2020. "Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations," MAGKS Papers on Economics 202034, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    4. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    5. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    6. Zhiyong Tu & Lan Ju, 2019. "A Normative Dual-value Theory for Bitcoin and other Cryptocurrencies," Papers 1904.05028, arXiv.org.
    7. Paola Stolfi & Mauro Bernardi & Davide Vergni, 2022. "Robust estimation of time-dependent precision matrix with application to the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    8. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    9. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
    10. Zhang, Wei & Li, Yi, 2020. "Is idiosyncratic volatility priced in cryptocurrency markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    11. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    12. Utku Altunoz, 2023. "Analyzing the Volatility Dynamics of Crypto Currency and the Occurrence of Speculative Bubbles: The Examples of Bitcoin, Ethereum, and Ripple," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 615-643, June.
    13. Khanh Hoang & Cuong C. Nguyen & Kongchheng Poch & Thang X. Nguyen, 2020. "Does Bitcoin Hedge Commodity Uncertainty?," JRFM, MDPI, vol. 13(6), pages 1-14, June.
    14. Ahmed, Walid M.A., 2021. "How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    15. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    16. Aysu Ahmadova & Taghi Guliyev & Khatai Aliyev, 2024. "The Relationship between Bitcoin and Nasdaq, U.S. Dollar Index and Commodities," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 281-289, January.
    17. Sercan Demiralay & Selçuk Bayracı, 2021. "Should stock investors include cryptocurrencies in their portfolios after all? Evidence from a conditional diversification benefits measure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6188-6204, October.
    18. José Antonio Núñez-Mora & Mario Iván Contreras-Valdez & Roberto Joaquín Santillán-Salgado, 2023. "Risk Premium of Bitcoin and Ethereum during the COVID-19 and Non-COVID-19 Periods: A High-Frequency Approach," Mathematics, MDPI, vol. 11(20), pages 1-20, October.
    19. Karim, Muhammad Mahmudul & Ali, Md Hakim & Yarovaya, Larisa & Uddin, Md Hamid & Hammoudeh, Shawkat, 2023. "Return-volatility relationships in cryptocurrency markets: Evidence from asymmetric quantiles and non-linear ARDL approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
    20. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    21. Wang, Weichen & An, Ran & Zhu, Ziwei, 2024. "Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective," Journal of Econometrics, Elsevier, vol. 239(2).
    22. Demiralay, Sercan & Golitsis, Petros, 2021. "On the dynamic equicorrelations in cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 524-533.
    23. Pinar Deniz & Thanasis Stengos, 2020. "Cryptocurrency Returns before and after the Introduction of Bitcoin Futures," JRFM, MDPI, vol. 13(6), pages 1-21, June.

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    More about this item

    Keywords

    Bitcoin;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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