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Forecasting bitcoin volatility: Evidence from the options market

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  • Lai T. Hoang
  • Dirk G. Baur

Abstract

This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressive–Moving‐Average or Heterogeneous Auto‐Regressive model forecasts in predicting short‐term BTC volatility (1 day ahead), but superior in predicting long‐term volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and model‐based forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.

Suggested Citation

  • Lai T. Hoang & Dirk G. Baur, 2020. "Forecasting bitcoin volatility: Evidence from the options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1584-1602, October.
    • Handle: RePEc:wly:jfutmk:v:40:y:2020:i:10:p:1584-1602
    DOI: 10.1002/fut.22144
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    3. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    4. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    6. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.
    7. 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).
    8. Weige Huang & Xiang Gao, 2023. "Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies," SAGE Open, , vol. 13(1), pages 21582440231, January.
    9. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    10. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.
    11. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

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