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

  • Andrea Cipollini

    (Queen Mary, University of London)

  • George Kapetanios


    (Queen Mary, University of London)

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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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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  1. Mardi Dungey & Vance L Martin & Adrian R Pagan, 2000. "A multivariate latent factor decomposition of international bond yield spreads," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 697-715.
  2. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.
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