Predicting the Volatility of Cryptocurrency Timeï¿½Series
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
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 ï¿½efficientï¿½, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrenciesï¿½ volatility has been found to exhibit at the same time differences and similarities with other financial timeï¿½series, 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.
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