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dc.contributor.authorMunim, Ziaul Haque
dc.contributor.authorShakil, Mohammad Hassan
dc.contributor.authorAlon, Ilan
dc.date.accessioned2020-03-23T19:20:56Z
dc.date.available2020-03-23T19:20:56Z
dc.date.created2019-07-08T14:34:36Z
dc.date.issued2019
dc.identifier.citationMunim, Z. H., Shakil, M. H. & Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management. 12(2),15.en_US
dc.identifier.issn1911-8074
dc.identifier.urihttps://hdl.handle.net/11250/2648227
dc.description.abstractThis study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleNext-Day Bitcoin Price Forecasten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2019 The Author(s)en_US
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212en_US
dc.source.pagenumber15en_US
dc.source.volume12en_US
dc.source.journalJournal of Risk and Financial Managementen_US
dc.source.issue2en_US
dc.identifier.doi10.3390/jrfm12020103
dc.identifier.cristin1710651
cristin.qualitycode1


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