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

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.