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Predicting the Volatility of Cryptocurrency Time-Series

  • Leopoldo Catania
  • Stefano Grassi
  • Francesco Ravazzolo
Chapter

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.

Keywords

Cryptocurrencies Score-driven models Volatility forecast 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Leopoldo Catania
    • 1
  • Stefano Grassi
    • 2
  • Francesco Ravazzolo
    • 3
    • 4
  1. 1.Department of Economics and Business EconomicsAarhus BSS and CREATESAarhus NDenmark
  2. 2.Department of Economics and FinanceUniversity of Rome Tor Vergata and CREATESRomeItaly
  3. 3.Faculty of Economics and ManagementFree University of Bozen-BolzanoBolzanoItaly
  4. 4.CAMPBI Norwegian Business SchoolOsloNorway

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