Predicting the Volatility of Cryptocurrency Time�Series

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

No No 3/2018, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School

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: one day, one and two weeks volatility levels.

Pages: 7
Date: 2018-02
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