Modeling of stock indices using asymmetric GARCH models
Abstract
This study discusses GARCH modeling with a special focus on the fitting of GARCH models to financial
return series. Most popular asymmetric GARCH models are considered by comparing the modeling
performance of different conditional variance models. The data consist of daily closing levels of indices
for the Nairobi Stock Exchange(NSE) running through 1996 to 2003 with reference to the equity of
Uchumi Supermarket. The results suggest that improvement of the overall estimation are achieved when
asymmetric GARCH are used and when fat-tailed densities are taken into account in the conditional
variance.
Citation
M.Sc (Mathematical Statistics) ThesisSponsorhip
University of NairobiPublisher
School of Mathematics, University of Nairobi
Description
Master of Science Thesis