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This document is the thesis submitted by Xiaorong Yang to the graduate program in Mathematics at the University of Kansas to earn a Master of Arts degree. The thesis examines forecasting volatility in the stock market using generalized autoregressive conditional heteroscedasticity (GARCH) models. Specifically, it compares the GARCH(P,Q) model, GJR-GARCH(P,Q) model, and EGARCH(P,Q) model and applies them to Dow Jones Index data. The thesis finds that the GJR-GARCH(P,Q) model is more powerful at capturing lev

Author

ramzi

Language

EN