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Forecasting Realized Volatility of Bitcoin Returns: Tail Events and Asymmetric Loss


  • Konstantinos Gkillas

    (Department of Business Administration, University of Patras-University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B.700822, 22008 Hamburg, Germany)


We use intra-day data to construct measures of the realized volatility of bitcoin returns. We then use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether indices which capture the tail behaviour (heavy-tailedness and asymmetry) of the daily returns distribution help to forecast subsequent realized volatility. We find that mainly forecasters who suffer a higher loss in case of an underprediction of realized volatility than in case of an overprediction of the same absolute size benefit from using the tail indices as predictors of realized volatility at intermediate forecast horizons. This result is robust to controlling for realized skewness and realized kurtosis, and it also applies to “bad” and “good” realized volatility.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Volatility of Bitcoin Returns: Tail Events and Asymmetric Loss," Working Papers 201905, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201905

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    Cited by:

    1. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Muhammad Ikhlas Rosele & Abdul Muneem & Azizi Bin Che Seman & Luqman Bin Haji Abdullah & Noor Naemah Binti Abdul Rahman & Mohd Edil Bin Abd Sukor & Abdul Karim Bin Ali, 2022. "The Concept of Wealth (mÄ l) in the SharÄ«Ê¿ah and Its Relation to Digital Assets," SAGE Open, , vol. 12(2), pages 21582440221, June.
    3. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    4. 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.
    5. Lei Wang & Provash Kumer Sarker & Elie Bouri, 2023. "Short- and Long-Term Interactions Between Bitcoin and Economic Variables: Evidence from the US," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1305-1330, April.
    6. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    7. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
    8. Arthur Jin Lin, 2023. "Volatility Contagion from Bulk Shipping and Petrochemical Industries to Oil Futures Market during the Economic Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-19, August.

    More about this item


    Bitcoin; Realized volatility; Forecasting; Tail events;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications


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