**6. Conclusions**

Recently, cryptocurrencies have attracted attention from researchers and financial institutions due to their importance. In this paper, a comparison of the performance of several models has been investigated to predict four of the most capitalised cryptocurrencies: Bitcoin, Ethereum, Ripple and Litecoin. A set of cryptopredictors is applied and eight model combinations are proposed for combining these predictors. The results show statistically significant improvements in point forecasting for all the cryptocurrencies when using a combination of stochastic volatility and a student-t distribution. In density forecasting for all cryptocurrencies, the stochastic volatility model gives the best predictability. One recommendation for future research is to allow different weights across time and time-varying parameters to improve the point and density forecasting. Moreover, other cryptopredictors based on the dynamics of the cryptomarket might be interesting for modelling.

**Author Contributions:** The work was equally divided between the two coauthors. The origin and development of the paper was a joint initiative. R.B. focused on collection of data and econometric analysis; L.R. worked on writing results and the working paper.

**Funding:** This research was supported by the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie gran<sup>t</sup> agreemen<sup>t</sup> No 796902.

**Acknowledgments:** We thank the editor, two anonymous referees and Lennart Hoogerheide for helpful comments and suggestions to improve this work. Luca Rossini acknowledges financial support from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie gran<sup>t</sup> agreemen<sup>t</sup> No 796902.

**Conflicts of Interest:** The authors declare no conflict of interest.
