Department of
presents
YazhenWang
National Science Foundation and
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 wavelet methods for analyzing jump and volatility
variations and the matrix
factor model for handling large size volatility matrices.
Friday, February 29, 2008
3:35 - 4:15 pm
206 Cox Hall
Refreshments will be served in the common area of 222 Dabney Hall at 3:00 pm.