We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.

翻译：本研究探讨加密货币风险价值（VaR）的预测问题。与传统资产相比，加密货币收益率通常具有极高波动性，且常呈现围绕单一事件的大幅波动特征。通过对105种主要加密货币进行综合分析，我们发现适用于分位数预测的广义随机森林（GRF）方法（Athey, Tibshirani & Wager, 2019）在预测性能上优于分位数回归、GARCH类模型及CAViaR模型等传统方法。该优势在市场不稳定时期及高波动性加密货币类别中尤为显著。此外，我们识别了此类时期的关键预测变量，并揭示了其对时序预测的影响机制。综合模拟研究进一步表明，在标准金融收益类型的VaR预测中，GRF方法至少与现有方法性能相当；而在加密货币场景中则具有明显优越性。