Statistics and Data Science Seminar
Prof. Yazhen Wang
National Science Foundation and University of Connecticut
Modeling and Analyzing High-Frequency Financial Data
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 multi-scale methods for analyzing jump and volatility variations and matrix factor models for handling large size volatility matrices.
Thursday May 1, 2008 at 3:00 PM in SEO 636