Predicting the Volatility of Cryptocurrency Time-Series

Leopoldo Catania (), Stefano Grassi () and Francesco Ravazzolo ()
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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

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 203-207 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_37

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DOI: 10.1007/978-3-319-89824-7_37

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