Speaker: 施淑萍 (Shuping Shi)

Title: Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications

Schedule: May 10, Wed 1:30-3:00 PM

Location:  Zoom Meeting: 964 861 1075. Password: 123

 

Introduction: 施淑萍，麦考瑞大学经济学系教授。主要研究兴趣包括计量经济学理论、金融计量经济学、房地产泡沫等多个方面。曾在 Management Science、Journal of Econometrics、International Economic Review、Econometric Theory、Journal of Financial Econometrics、Journal of Banking and Finance、Journal of Economic Surveys 等国际核心期刊发表论文20余篇。施淑萍教授致力于将理论运用于实践，她及其合作者提出的金融泡沫的检验方法, 以及估计泡沫产生的时间和危机发生的时间, 已经引起一些中央银行的高度关注。她为澳大利亚各州首府以及新西兰六大主区建立了房价泡沫的实时监测系统，与此同时，她的实验室还为世界25个国家的房价指数提供实时监测。因其杰出的研究成果和影响力，施淑萍教授于2022年获得 “澳大利亚年度青年经济学家” 的荣誉。

 

Abstract: We introduce a simple tool to control for false discoveries and identify individual signals when there are many tests, the test statistics are correlated, and the signals are potentially sparse. The tool applies the Cauchy combination test recursively on a sequence of expanding subsets of p-values and is referred to as the sequential Cauchy combination test. While the original Cauchy combination test aims for a global statement over a set of null hypotheses by summing transformed p-values, the sequential version determines which p-values trigger the rejection of the global null. The test achieves strong family wise error rate control and is less conservative than existing controlling procedures when the test statistics are dependent, leading to higher global powers and successful detection rates. As illustrations, we consider two popular financial econometric applications for which the test statistics have either serial dependence or cross-sectional dependence: monitoring drift bursts in asset prices and searching for assets with a nonzero alpha. The sequential Cauchy combination test is a preferable alternative in both cases in simulation settings and leads to higher detection rates than benchmark procedures in empirics.