Forecasting Cryptocurrencies Under Model and Parameter Instability

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Forecasting Cryptocurrencies Under Model and Parameter Instability. / Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco.

In: International Journal of Forecasting, Vol. 35, No. 2, 2019, p. 485-501.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearch

Harvard

Catania, L, Grassi, S & Ravazzolo, F 2019, 'Forecasting Cryptocurrencies Under Model and Parameter Instability', International Journal of Forecasting, vol. 35, no. 2, pp. 485-501. https://doi.org/10.1016/j.ijforecast.2018.09.005

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Author

Catania, Leopoldo ; Grassi, Stefano ; Ravazzolo, Francesco. / Forecasting Cryptocurrencies Under Model and Parameter Instability. In: International Journal of Forecasting. 2019 ; Vol. 35, No. 2. pp. 485-501.

Bibtex

@article{7506c5b848944068b810f162cee5f08f,
title = "Forecasting Cryptocurrencies Under Model and Parameter Instability",
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 severalmultivariate 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.",
author = "Leopoldo Catania and Stefano Grassi and Francesco Ravazzolo",
year = "2019",
doi = "10.1016/j.ijforecast.2018.09.005",
language = "English",
volume = "35",
pages = "485--501",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - Forecasting Cryptocurrencies Under Model and Parameter Instability

AU - Catania, Leopoldo

AU - Grassi, Stefano

AU - Ravazzolo, Francesco

PY - 2019

Y1 - 2019

N2 - 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 severalmultivariate 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.

AB - 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 severalmultivariate 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.

U2 - 10.1016/j.ijforecast.2018.09.005

DO - 10.1016/j.ijforecast.2018.09.005

M3 - Journal article

VL - 35

SP - 485

EP - 501

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 2

ER -