Forecasting Volatility in Stock Market Using GARCH Models

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Issue Date
2008-01-01Author
Yang, Xiaorong
Publisher
University of Kansas
Format
43 pages
Type
Thesis
Degree Level
M.A.
Discipline
Mathematics
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This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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Show full item recordAbstract
Forecasting volatility has held the attention of academics and practitioners all over the world. The objective for this master's thesis is to predict the volatility in stock market by using generalized autoregressive conditional heteroscedasticity(GARCH) methodology. A detailed explanation of GARCH models is presented and empirical results from Dow Jones Index are discussed. Different from other literatures in this field, this paper studies forecasting volatility from a new perspective by comparing GARCH(P,Q) model with GJR-GARCH(P,Q) model and EGARCH(P,Q) model. GJR-GARCH(P,Q) model turns out to be more powerful than GARCH(P,Q) model due to catching some leverage effects successfully. This makes our prediction more reliable and accurate. This paper also shows that both GARCH(P,Q) model and GJR-GARCH(P,Q) model are good choices for dealing with heteroscedastic time series.
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- Mathematics Dissertations and Theses [179]
- Theses [3901]
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