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Predicting the Volatility of Cryptocurrency Time-Series

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Leopoldo Catania

    (Aarhus BSS and CREATES, Department of Economics and Business Economics)

  • Stefano Grassi

    (University of Rome Tor Vergata and CREATES, Department of Economics and Finance)

  • Francesco Ravazzolo

    (Free University of Bozen-Bolzano, Faculty of Economics and Management
    BI Norwegian Business School, CAMP)

Abstract

Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being “efficient”, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies’ volatility has been found to exhibit at the same time differences and similarities with other financial time-series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investigate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: 1 day, 1 and 2 weeks volatility levels.

Suggested Citation

  • Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time-Series," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 203-207, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_37
    DOI: 10.1007/978-3-319-89824-7_37
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