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Random Correlation Matrix and De-Noising

  • Ken-ichi Mitsui

    ()

    (Doctor Candidate of Osaka University)

  • Yoshio Tabata

    ()

    (Graduate School of Business Administration, Nanzan Univeristy)

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    In Finance, the modeling of a correlation matrix is one of the important problems. In particular, the correlation matrix obtained from market data has the noise. Here we apply the de-noising processing based on the wavelet analysis to the noisy correlation matrix, which is generated by a parametric function with random parameters. First of all, we show that two properties, i.e. symmetry and ones of all diagonal elements, of the correlation matrix preserve via the de-noising processing and the efficiency of the de-nosing processing by numerical experiments. We propose that the de-noising processing is one of the effective methods in order to reduce the noise in the noisy correlation matrix.

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    File URL: http://www2.econ.osaka-u.ac.jp/library/global/dp/0626.pdf
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    Paper provided by Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP) in its series Discussion Papers in Economics and Business with number 06-26.

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    Length: 25 pages
    Date of creation: Sep 2006
    Date of revision:
    Handle: RePEc:osk:wpaper:0626
    Contact details of provider: Web page: http://www.econ.osaka-u.ac.jp/Email:


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    1. Igor Grubisic & Raoul Pietersz, 2005. "Efficient Rank Reduction of Correlation Matrices," Finance 0502007, EconWPA.
    2. repec:ner:tilbur:urn:nbn:nl:ui:12-3159390 is not listed on IDEAS
    3. Frank de Jong & Joost Driessen & Antoon Pelsser, 2004. "On the Information in the Interest Rate Term Structure and Option Prices," Review of Derivatives Research, Springer, vol. 7(2), pages 99-127, 08.
    4. Massimo Morini & Nick Webber, 2006. "An EZI Method to Reduce the Rank of a Correlation Matrix in Financial Modelling," Applied Mathematical Finance, Taylor & Francis Journals, vol. 13(4), pages 309-331.
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