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Forecasting cryptocurrencies under model and parameter instability

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  • Catania, Leopoldo
  • Grassi, Stefano
  • Ravazzolo, Francesco

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.

Suggested Citation

  • Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:485-501
    DOI: 10.1016/j.ijforecast.2018.09.005
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