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dc.contributor.authorCatania, Leopoldo
dc.contributor.authorGrassi, Stefano
dc.contributor.authorRavazzolo, Francesco
dc.date.accessioned2018-03-08T07:32:46Z
dc.date.available2018-03-08T07:32:46Z
dc.date.issued2018-03
dc.identifier.issn1892-2198
dc.identifier.urihttp://hdl.handle.net/11250/2489408
dc.description.abstractThis 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.nb_NO
dc.language.isoengnb_NO
dc.publisherBI Norwegian Business School, Centre for Applied Macro- and Petroleum Economicsnb_NO
dc.relation.ispartofseriesCAMP Working Paper Series;5
dc.subjectCryptocurrencynb_NO
dc.subjectBitcoinnb_NO
dc.subjectForecastingnb_NO
dc.subjectDensity Forecastingnb_NO
dc.subjectVARnb_NO
dc.subjectDynamic Model Averagingnb_NO
dc.titleForecasting Cryptocurrencies Financial Time Seriesnb_NO
dc.typeWorking papernb_NO
dc.source.pagenumber28nb_NO


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