*Article* **Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models**

#### **Rick Bohte and Luca Rossini \***

School of Business and Economics, Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands; r.bohte@student.vu.nl

Received: 9 September 2019; Accepted: 14 September 2019; Published: 18 September 2019

**Abstract:** This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalised cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some cryptopredictors are included in the analysis, such as S&P 500 and Nikkei 225. In this paper, the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility, the student-t distribution is shown to outperform the standard normal approach.

**Keywords:** Bayesian VAR; cryptocurrency; Bitcoin; forecasting; density forecasting; time-varying volatility
