Bootstrapping High-Frequency Jump Tests
Bootstrapping High-Frequency Jump Tests
复制标题
自举高频跳跃测试
DOI：
10.1080/01621459.2018.1447485
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发表时间：
2018
影响因子：
3.7
通讯作者：
Nour Meddahi
中科院分区：
文献类型：
--
作者：
P. Dovonon;Sílvia Gonçalves;Ulrich Hounyo;Nour Meddahi
摘要本文的主要贡献是提出了一种基于已实现波动率和双幂变差函数的跳跃自举检验方法。Bootstrap日内收益率是从均值为零的高斯分布随机生成的，方差由综合波动率的局部度量（我们表示为）给出。我们首先讨论了一组高层次的条件，使任何这种形式的自助测试有正确的渐近大小，是交替一致的。然后，我们提供了一组原始的条件，证明选择一个基于阈值的估计。我们的累积展开表明，自助法无法模拟检验统计量的高阶偏差。我们提出了一个修改的原始自助测试，其中包含一个适当的偏差校正项，并获得二阶渐近细化。
ABSTRACT 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 ). We first discuss a set of high-level conditions on 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 . 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.