报告题目：Nonparametric Estimation of Large Spot Volatility Matricesfor High-Frequency Financial Data
报告人：王汉超教授（山东大学金融研究院）
邀请人：宗高峰副教授
主持人：李娜教授，统计与数学学院副院长
报告地点：舜耕校区统计与数学学院315会议室
报告时间：2024年10月13日（星期日）16:00-17:00
主办单位：山东财经大学统计与数学学院
摘要：In thistalk, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for many assets. We first combine classic nonparametric kernel-based smoothing with a generalized shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the high-frequency data contaminated by microstructure noise, we introduce a localized pre-averaging estimation method that reduces the effective magnitude of the noise. We then use the estimation tool developed in the noise-free scenario and derive the uniform convergence rates for the developed spot volatility matrix estimator. We further combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector. In addition, we consider large spot volatility matrix estimation in time-varying factor models with observable risk factors and derive the uniform convergence property. We provide numerical studies, including simulation and empirical application, to examine the performance of the proposed estimation methods in finite samples. This is a joint work with Bu Ruijun, Li Degui and Oliver Linton.
报告人简介：
王汉超，山东大学金融研究院教授，博士生导师。2011年于浙江大学数学系概率论与数理统计专业博士毕业。主要从事概率统计极限理论及其应用的研究，特别在弱收敛，集中不等式与金融统计等领域发表了若干文章。与林正炎教授合作，在新加坡世界科技出版社出版专著 Weak Convergence and Its Applications。近几年来，在概率论，数理统计，计量经济等领域权威期刊上发表论文近二十篇。主持国家自然科学基金两项，作为骨干成员，参加国家重点研发计划项目两项。受邀先后访问过澳大利亚悉尼大学，俄罗斯斯捷克罗夫数学研究所，莫斯科国立大学，新加坡国立大学，加拿大菲尔兹数学研究所，香港浸会大学，加拿大滑铁卢大学，英国约克大学等。