主题：Bootstrapping Laplace Transforms of Volatility
主讲人：刘志 副教授（澳门大学）
主持人：刘一鸣 助理教授（太阳城集团）
会议时间：2022年9月21 日（周三）上午9：00—10：00
会议工具：腾讯会议（ID：614-979-257）
摘要
This paper studies inference for the realized Laplace transform (RLT) of volatility in a fixed-span setting using bootstrap methods. Specifically, since standard wild bootstrap procedures deliver inconsistent inference, we propose a local Gaussian (LG) bootstrap, establish its first-order asymptotic validity, and use Edgeworth expansions to show that the LG bootstrap inference achieves second-order asymptotic refinements. Moreover, we provide new Laplace transform-based estimators of the spot variance as well as the covariance, correlation and beta between two semimartingales, and adapt our bootstrap procedure to the requisite scenario. We establish central limit theory for our estimators and first-order asymptotic validity of their associated bootstrap methods. Simulations demonstrate that the LG bootstrap outperforms existing feasible inference theory and wild bootstrap procedures in finite samples. Finally, we illustrate the use of the new methods by examining the coherence between stocks and bonds during the global financial crisis of 2008 as well as the COVID-19 pandemic stock sell-off during 2020, and by a forecasting exercise.
主讲人简介
刘志，2011年博士毕业于香港科技大学，2011年8月---2012年8月任厦门大学王亚南经济研究院与tyc1286太阳成集团双聘助理教授，2012年8月起先后任澳门大学数学系助理教授，副教授。IMS,AFA,EconometricSociety会员。主要研究方向有：金融高频数据分析、金融风险管理、随机过程统计推断，生物信息等。已在AoS，Jasa，JoE，Jbes，Bioinformatics，ET等相关研究方向的权威期刊发表论文40多篇。
