Bootstrapping High-Frequency Jump Tests
本文提出一种基于已实现波动率和双幂次变差的Bootstrap跳跃检验方法，通过随机生成日内收益率来构造检验，并证明了其渐近性质，适用于高频金融数据分析。
The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by {v^in}). We first discuss a set of high-level conditions on {v^in} such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for {v^in}. Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.