Forecasting Cryptocurrencies Financial Time Series
Leopoldo Catania (),
Stefano Grassi () and
Francesco Ravazzolo ()
No No 5/2018, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School
This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical significant improvements in point forecasting when using combinations of univariate models and in density forecasting when relying on selection of multivariate models.
Keywords: Cryptocurrency; Bitcoin; Forecasting; Density Forecasting; VAR; Dynamic Model Averaging (search for similar items in EconPapers)
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