dc.contributor.author | Catania, Leopoldo | |
dc.contributor.author | Grassi, Stefano | |
dc.contributor.author | Ravazzolo, Francesco | |
dc.date.accessioned | 2019-12-19T12:46:21Z | |
dc.date.available | 2019-12-19T12:46:21Z | |
dc.date.created | 2019-05-14T19:14:38Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | International Journal of Forecasting. 2019, 35 (2), 485-501. | nb_NO |
dc.identifier.issn | 0169-2070 | |
dc.identifier.uri | http://hdl.handle.net/11250/2634153 | |
dc.description.abstract | This paper studies the predictability of cryptocurrency time series. We compare several alternative univariate and multivariate models for 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 statistically significant improvements in point forecasting when using combinations of univariate models, and in density forecasting when relying on the selection of multivariate models. Both schemes deliver sizable directional predictability. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Elsevier | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Forecasting cryptocurrencies under model and parameter instability | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.rights.holder | Copyright policy of Elsevier, the publisher of this journal. The author retains the right to post the accepted author manuscript on open web sites operated by author or author's institution for scholarly purposes, with an embargo period of 0-36 months after first view online. | nb_NO |
dc.source.pagenumber | 485-501 | nb_NO |
dc.source.volume | 35 | nb_NO |
dc.source.journal | International Journal of Forecasting | nb_NO |
dc.source.issue | 2 | nb_NO |
dc.identifier.doi | 10.1016/j.ijforecast.2018.09.005 | |
dc.identifier.cristin | 1697908 | |
cristin.unitcode | 158,0,0,0 | |
cristin.unitname | Handelshøyskolen BI | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |