Large-scale multiple testing under static factor models is commonly used to select skilled funds in financial market. However, static factor models are arguably too stringent as it ignores the serial correlation, which severely distorts error rate control in large-scale inference. In this manuscript, we propose a new multiple testing procedure under dynamic factor models that is robust against both heavy-tailed distributions and the serial dependence. The idea is to integrate a new sample-splitting strategy based on chronological order and a two-pass Fama-Macbeth regression to form a series of statistics with marginal symmetry properties and then to utilize the symmetry properties to obtain a data-driven threshold. We show that our procedure is able to control the false discovery rate (FDR) asymptotically under high-dimensional dynamic factor models. As a byproduct that is of independent interest, we establish a new exponential-type deviation inequality for the sum of random variables on a variety of functionals of linear and non-linear processes. Numerical results including a case study on hedge fund selection demonstrate the advantage of the proposed method over several state-of-the-art methods.

翻译：静态因子模型下的大规模多重检验常被用于金融市场中筛选优秀基金。然而，静态因子模型因忽略序列相关性而可能过于严苛，这在大规模推断中会严重扭曲错误率控制。本文提出了一种基于动态因子模型的新多重检验程序，该程序对重尾分布和序列依赖性均具有稳健性。其核心思想是：整合基于时间顺序的样本分割策略与两阶段法玛-麦克白回归，构建具有边际对称性的一系列统计量；进而利用对称性推导出数据驱动的阈值。我们证明该程序能在高维动态因子模型下渐近控制错误发现率（FDR）。作为一项具有独立价值的副产品，我们针对线性及非线性过程各类泛函的随机变量之和建立了新的指数型偏差不等式。数值结果（包括对冲基金筛选的案例研究）表明，所提方法优于多种现有先进方法。