EconPapers    
Economics at your fingertips  
 

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

Abstract: 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)
Pages: 28
Date: 2018-03
New Economics Papers: this item is included in nep-for and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
https://brage.bibsys.no/xmlui/bitstream/handle/112 ... quence=1&isAllowed=y

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0063

Access Statistics for this paper

More papers in Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School Contact information at EDIRC.
Bibliographic data for series maintained by Helene Olsen ().

 
Page updated 2022-05-20
Handle: RePEc:bny:wpaper:0063