Department of Economics and Business Economics

Leopoldo Catania

Forecasting Cryptocurrencies Under Model and Parameter Instability

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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.
Original languageEnglish
JournalInternational Journal of Forecasting
Pages (from-to)485-501
Number of pages17
Publication statusPublished - 2019

    Research areas

  • Bitcoin, Cryptocurrency, Density forecasting, Dynamic model averaging, Forecasting, VAR

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