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Leopoldo Catania

Predicting the Volatility of Cryptocurrency Time–Series

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

Cryptocurrencies have recently gained a lot of interest from investors,
central banks and governments worldwide. The lack of any form of political regu-
lation and their market far from being “efficient”, require new forms of regulation
in the near future. From an econometric viewpoint, the process underlying the evo-
lution 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 investi-
gate 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: one day, one and two
weeks volatility levels.
Original languageEnglish
Title of host publicationMathematical and Statistical Methods for Actuarial Sciences and Finance
EditorsMarco Corazza, María Durbán, Aurea Grané, Cira Perna, Marilena Sibillo
Number of pages5
Publication year2018
ISBN (print)978-3-319-89823-0
ISBN (Electronic)978-3-319-89824-7
Publication statusPublished - 2018
EventMAF Conference - Universidad Carlos III, Madrid, Spain
Duration: 4 Apr 20186 Apr 2018


ConferenceMAF Conference
LocationUniversidad Carlos III

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Selected short papers

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ID: 123349485