Modeling and Analyzing High-Frequency Financial Data
主 题: Modeling and Analyzing High-Frequency Financial Data
报告人: Yazhen Wang（University of Wisconsin-Madison）
时 间: 2014-03-11 16:00-17:00
地 点: 理科一号楼1114（统计中心活动）
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.