Statistics and Data Science Seminar
Prof. Fangfang Wang
UIC
The HYBRID GARCH Class of Models
Abstract: We propose a general GARCH framework that allows the use of different
frequency returns to model conditional heteroskedasticity. We call the class of models High
FrequencY Data-Based PRojectIon-Driven GARCH models as the GARCH dynamics are driven by
what we call HYBRID processes. We study three broad classes of HYBRID processes: (1)
parameter-free processes that are purely data-driven, (2) structural HYBRIDs where one
assumes an underlying DGP for the high frequency data and finally (3) HYBRID filter
processes. We develop the asymptotic theory of various estimators and study their
properties in small samples via simulations.
This is joint work with Eric Ghysels (University of North Carolina at Chapel
Hill) and Xilong Chen (SAS Institute Inc.).
Wednesday March 31, 2010 at 3:00 PM in SEO 636