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We study the prediction of Value at Risk (VaR) for cryptocurrencies. Incontrast to classic assets, returns of cryptocurrencies are often highlyvolatile and characterized by large fluctuations around single events.Analyzing a comprehensive set of 105 major cryptocurrencies, we show thatGeneralized Random Forests (GRF) (Athey et al., 2019) adapted to quantileprediction have superior performance over other established methods such asquantile regression, GARCH-type and CAViaR models. This advantage is especiallypronounced in unstable times and for classes of highly-volatilecryptocurrencies. Furthermore, we identify important predictors during suchtimes and show their influence on forecasting over time. Moreover, acomprehensive simulation study also indicates that the GRF methodology is atleast on par with existing methods in VaR predictions for standard types offinancial returns and clearly superior in the cryptocurrency setup.

Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests