Forecasting the Risk of Cryptocurrencies : Comparison and Combination of GARCH and Stochastic Volatility Models

We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t-distributed innovations or allowing for regime changes improves the accuracy in both model classes. Moreover, we find that Markov switching stochastic volatility models perform in particular well. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk