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A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data

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Author Info

  • Andrea Cipollini

    (Queen Mary, University of London)

  • George Kapetanios

    ()
    (Queen Mary, University of London)

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    Abstract

    The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest use of the principal component methodology of Stock and Watson (2002) for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard (1994). The method is simple and computationally tractable for very large datasets. We provide theoretical results on this method and apply it to S&P data.

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    File URL: http://www.econ.qmul.ac.uk/papers/doc/wp506.pdf
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    Bibliographic Info

    Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 506.

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    Date of creation: Feb 2004
    Date of revision:
    Handle: RePEc:qmw:qmwecw:wp506

    Note: A revised version is available at the personal homepage of George Kapetanios .
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    Postal: London E1 4NS
    Phone: +44 (0) 20 7882 5096
    Fax: +44 (0) 20 8983 3580
    Web page: http://www.econ.qmul.ac.uk
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    Related research

    Keywords: Stochastic volatility; Factor models; Principal components;

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    Cited by:
    1. Mónica Fuentes & Sergio Godoy, 2005. "Sovereign Spread in Emerging Markets: A Principal Component Analysis," Working Papers Central Bank of Chile 333, Central Bank of Chile.
    2. Silvia S.W. Lui, 2006. "An Empirical Study of Asian Stock Volatility Using Stochastic Volatility Factor Model: Factor Analysis and Forecasting," Working Papers 581, Queen Mary, University of London, School of Economics and Finance.

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