论文标题
预测具有广义随机森林的加密货币的风险
Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests
论文作者
论文摘要
我们研究加密货币处于风险（VAR）的价值预测。与经典资产相反，加密货币的回报通常是高度挥发性的，其特征是单个事件周围的波动很大。分析一组全面的105个主要加密货币，我们表明，适用于分位数预测的广义随机森林（GRF）（Athey，Tibshirani＆Wager，2019年）比其他既定方法，例如分位数回归，Garch-type和Caviar模型具有较高的性能。在不稳定的时期和一系列高极度的加密货币的类别中，这种优势尤其明显。此外，我们在此期间确定了重要的预测指标，并显示了它们对随着时间的预测的影响。此外，一项全面的模拟研究还表明，GRF方法至少与标准财务回报类型的VAR预测中的现有方法相当，并且在加密货币设置中显然优越。
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.
