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    Simulation Methods Comparison and Parameter Estimation for a Fractional Stochastic Volatility Model with Application in Stock Price Analysis

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    Wu_ku_0099M_13016_DATA_1.pdf (8.665Mb)
    Issue Date
    2013-08-31
    Author
    Wu, Fengmei
    Publisher
    University of Kansas
    Format
    58 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
    This paper studies continuous-time stock pricing models with stochastic volatility driven by fractional Brownian motion. We compare two ways for simulating the paths of stochastic volatility and stock price when the Hurst parameter of fractional Brown motion is between 0.5 and 1. The first approach, is to use truncated fractional Brownian motion to approximate the fractional Brownian motion and estimate the volatility by Monte Carlo integral and symbolic integral. In the second one, Euler method is employed in simulation, without truncating the fractional Brownian process. Simulating the fractional Brownian motion in the second approach, we use spectral representation. Simulation results show that the latter is more efficient than using the symbolic integral and Monte Carlo integral is the worst. The application of the stochastic model is illustrated through real financial data.
    URI
    http://hdl.handle.net/1808/12221
    Collections
    • Mathematics Dissertations and Theses [179]
    • Theses [3828]

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    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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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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