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Survey of Cryptocurrency Volatility Prediction Literature Using Artificial Neural Networks

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  • Sina E. Charandabi
  • Kamyar Kamyar

Abstract

We start by presenting a short description of the concept of cryptocurrency and the history behind it. Recently-developed literature that attempt to predict volatilities of cryptocurrency valuations through creation of hybrid artificial neural network models are then discussed. For the major part of the paper, we delve into details of multiple hybrid artificial neural networks that were thoroughly implemented to predict cryptocurrency volatilities. Results are reported within the form of a survey. Finally, we compare different methods and discuss their results follow at the end.

Suggested Citation

  • Sina E. Charandabi & Kamyar Kamyar, 2022. "Survey of Cryptocurrency Volatility Prediction Literature Using Artificial Neural Networks," Business and Economic Research, Macrothink Institute, vol. 12(1), pages 1727-1727, December.
    • Handle: RePEc:mth:ber888:v:12:y:2022:i:1:p:1727
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    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    3. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    4. Mohsen MOHAGHEGH & A.S. VALIPOUR, 2020. "Income-dependent impacts of financial development and human capital on economic growth. A non-stationary panel analysis," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(625), W), pages 263-274, Winter.
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    Cited by:

    1. Dorien Herremans & Kah Wee Low, 2022. "Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models," Papers 2211.08281, arXiv.org.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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