.png){kind=icon}
.png){kind=icon}
Return
Forecasting cryptocurrencies under model and parameter instability
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijforecast.2018.09.005.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Cryptocurrency
Bitcoin
Forecasting
Density forecasting
VAR
Dynamic model averaging
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
3.1K
Citations:
9.9K
Organization
View more organizations