我们非常荣幸的邀请到美国威斯康星大学统计系 Wang Yazhen 教授来我院做关于高频金融数据建模与分析的学术报告。欢迎广大教师和研究生参加。 有关Wang Yzzhen 教授信息请见http://pages.cs.wisc.edu/~yzwang/
时 间：2014年3月11日（星期二上午10：00—11:00），
地 点：中国人民大学明德主楼1030教室
题 目： Modeling and Analyzing High-Frequency Financial Data
报告人： Yazhen Wang, University of Wisconsin-Madison
Abstract
Volatilities of asset returns are central to the theory and practice of asset pricing, portfolio allocation, and risk management. In financial economics, there is extensive research on modeling and forecasting volatility up to the daily level based on Black-Scholes, diffusion, GARCH, stochastic volatility models and implied volatilities from option prices. Nowadays, thanks to technological innovations, high-frequency financial data are available for a host of different financial instruments on markets of all locations and at scales like individual bids to buy and sell, and the full distribution of such bids. The availability of high-frequency data stimulates an upsurge interest in statistical research on better estimation of volatility. This talk will start with a review on low-frequency financial time series and high-frequency financial data. Then I will introduce popular realized volatility computed from high-frequency financial data and present my work on large volatility matrix estimation.