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Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss

Author

Listed:
  • Konstantinos Gkillas
  • Rangan Gupta
  • Christian Pierdzioch

Abstract

We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the tail shape of the return distribution, and use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether these measures help to forecast subsequent realized volatility. We find that mainly forecasters suffering 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 measures as predictors of realized volatility, especially at a short and intermediate forecast horizon. This result is robust controlling for jumps and realized skewness and kurtosis, and it also applies to downside (bad) and upside (good) realized volatility.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss," The European Journal of Finance, Taylor & Francis Journals, vol. 27(16), pages 1626-1644, November.
    • Handle: RePEc:taf:eurjfi:v:27:y:2021:i:16:p:1626-1644
    DOI: 10.1080/1351847X.2021.1906728
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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. Rao, Amar & Dev, Dhairya & Kharbanda, Aeshna & Parihar, Jaya Singh & Sala, Dariusz, 2024. "Mineral policy and sustainable development goals: Volatility forecasting in the Global South's minerals market," Resources Policy, Elsevier, vol. 98(C).
    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. 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.
    5. Suwan (Cheng) Long & Ioannis Chatziantoniou & David Gabauer & Brian Lucey, 2024. "Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(13), pages 1470-1489, September.
    6. 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.
    7. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    8. Maki, Daiki, 2024. "Forecasting downside and upside realized volatility: The role of asymmetric information," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    9. 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.
    10. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
    11. Xiaolong Tang & Yuping Song & Xingrui Jiao & Yankun Sun, 2024. "On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2011-2033, May.
    12. 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.
    13. 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.
    14. 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

    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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