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    Multivariate Levy Processes for Financial Returns

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    No. of downloads: 1250

    Date
    2004-11-10
    Author
    Yen, Ju-Yi
    Advisor
    Madan, Dilip B
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    Abstract
    We apply a signal processing technique known as independent component analysis (ICA) to multivariate financial time series. The main idea of ICA is to decompose the observed time series into statistically independent components (ICs). We further assume that the ICs follow the variance gamma (VG) process. The VG process is evaluated by Brownian motion with drift at a random time given by a gamma process. We build a multivariate VG portfolio model and analyze empirical results of the investment.
    URI
    http://hdl.handle.net/1903/1985
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    DRUM is brought to you by the University of Maryland Libraries
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