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Volatility forecasting accuracy for Bitcoin

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  • Köchling, Gerrit
  • Schmidtke, Philipp
  • Posch, Peter N.

Abstract

We analyze the quality of Bitcoin volatility forecasting of GARCH-type models applying different volatility proxies and loss functions. We construct model confidence sets and find them to be systematically smaller for asymmetric loss functions and a jump robust proxy.

Suggested Citation

  • Köchling, Gerrit & Schmidtke, Philipp & Posch, Peter N., 2020. "Volatility forecasting accuracy for Bitcoin," Economics Letters, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:ecolet:v:191:y:2020:i:c:s0165176519304239
    DOI: 10.1016/j.econlet.2019.108836
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    References listed on IDEAS

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    Cited by:

    1. Monika Eisenbardt & Tomasz Eisenbardt, 2023. "Can Cryptocurrencies Be Feasibly Adopted as a National Currency? The Perspective of the Younger Generation," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 463-481.
    2. Ishtiaq Ahmad Bajwa & Shafiq Ur Rehman & Abid Iqbal & Zaheer Anwer & Murtaza Ashiq & Muhammad Ajmal Khan, 2022. "Past, Present and Future of FinTech Research: A Bibliometric Analysis," SAGE Open, , vol. 12(4), pages 21582440221, October.
    3. Emon Kalyan Chowdhury & Mohammad Nayeem Abdullah, 2024. "Gauging Demand for Cryptocurrency over the Economic Policy Uncertainty and Stock Market Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 37-55, July.
    4. 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).
    5. Biswas, Priti & Jain, Prachi & Maitra, Debasish, 2024. "Are shocks in the stock markets driven by commodity markets? Evidence from Russia-Ukraine war," Journal of Commodity Markets, Elsevier, vol. 34(C).
    6. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
    7. 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.
    8. 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.
    9. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    10. Yin, Libo & Nie, Jing & Han, Liyan, 2021. "Understanding cryptocurrency volatility: The role of oil market shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 233-253.
    11. 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).
    12. Fang, Tong & Su, Zhi & Yin, Libo, 2020. "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, vol. 71(C).
    13. Delia-Elena Diaconaşu & Seyed Mehdian & Ovidiu Stoica, 2022. "An analysis of investors’ behavior in Bitcoin market," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-18, March.
    14. 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).
    15. José Almeida & Tiago Cruz Gonçalves, 2022. "A Systematic Literature Review of Volatility and Risk Management on Cryptocurrency Investment: A Methodological Point of View," Risks, MDPI, vol. 10(5), pages 1-18, May.
    16. 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).
    17. Klender Cortez & Martha del Pilar Rodríguez-García & Samuel Mongrut, 2020. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    18. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    19. Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023. "Deep State-Space Model for Predicting Cryptocurrency Price," Papers 2311.14731, arXiv.org.
    20. Zhang, Chuanhai & Ma, Huan & Arkorful, Gideon Bruce & Peng, Zhe, 2023. "The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 86(C).

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    More about this item

    Keywords

    Bitcoin; Cryptocurrency; GARCH; Volatility; Model confidence set; Robust loss function;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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