报告题目： Estimating Factor-Based Spot Volatility Matrices with Noisy and Asynchronous High-Frequency Data
报告人：李德柜 英国约克大学
报告时间：2022年12月6日(周二) 16:30-18:00
腾讯会议 ID：375-8612-5504
内容摘要
With noisy and asynchronous high-frequency data collected for an ultra-large number of assets, we estimate high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure. A localised pre-averaging method is proposed to jointly tackle the microstructure noise and asynchronicity issues, and obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent with convergence rates affected by the estimation errors due to the microstructure noise, asynchronicity and latent factor structures. Numerical studies are provided to assess the performance of the developed methods.
主讲人简介
李德柜，现为英国约克大学数学系正教授及统计研究所主任，2008年获浙江大学理学博士学位，曾在澳大利亚阿德莱德大学经济系和莫纳什大学商学院从事博士后研究。主要的研究领域包括非参数统计学，（非平稳）时间序列分析，面板数据建模，金融计量经济学，高维统计学和计量经济学，并有数十篇论文发表于国际知名统计学和计量经济学刊物如AoS，JASA，JOE，JBES，ET等。2011年获澳大利亚科研委员会DECRA奖，曾受ARC，BA/Leverhulme Trust及Heilbronn Institute等机构的科研资助，现担任Econometric Theory,Journal of Time Series Analysis及Econometrics & Statistics等国际学术刊物的编委。
