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    Forecasting Volatility in Stock Market Using GARCH Models

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    Yang_ku_0099M_10105_DATA_1.pdf (355.6Kb)
    Issue Date
    2008-01-01
    Author
    Yang, Xiaorong
    Publisher
    University of Kansas
    Format
    43 pages
    Type
    Thesis
    Degree Level
    M.A.
    Discipline
    Mathematics
    Rights
    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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    Abstract
    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.
    URI
    http://hdl.handle.net/1808/4556
    Collections
    • Mathematics Dissertations and Theses [179]
    • Theses [3901]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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