论文标题
强大的仿冒品用于控制虚假发现，并申请债券回收率
Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates
论文作者
论文摘要
我们通过高度相关的数据来应对可变选择的挑战，这些数据经常存在于金融，经济学中，但也存在于复杂的自然系统中，例如天气。我们开发了仿制框架的强大版本，该版本解决了许多可能的影响因素和牢固的时间相关性的挑战。特别是，重复的子采样策略可以解决仿冒品的变异性和因素的依赖性。同时，我们还控制了所有可能值的网格上的错误发现的比例，从而减轻了特定错误发现水平的临时选择中所选因子的可变性。在公司债券回收率的应用程序中，我们在已知的标准驱动程序上确定了新的重要因素的新重要组。但是我们还表明，样本外，所得的稀疏模型具有与使用整个预测变量集的最先进的机器学习模型相似的预测能力。
We address challenges in variable selection with highly correlated data that are frequently present in finance, economics, but also in complex natural systems as e.g. weather. We develop a robustified version of the knockoff framework, which addresses challenges with high dependence among possibly many influencing factors and strong time correlation. In particular, the repeated subsampling strategy tackles the variability of the knockoffs and the dependency of factors. Simultaneously, we also control the proportion of false discoveries over a grid of all possible values, which mitigates variability of selected factors from ad-hoc choices of a specific false discovery level. In the application for corporate bond recovery rates, we identify new important groups of relevant factors on top of the known standard drivers. But we also show that out-of-sample, the resulting sparse model has similar predictive power to state-of-the-art machine learning models that use the entire set of predictors.
