A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
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.
|Date of creation:||Feb 2004|
|Note:||A revised version is available at the personal homepage of George Kapetanios .|
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Web page: http://www.econ.qmul.ac.uk
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Elsevier, vol. 100(1), pages 130-134, July.
- Andrea Cipollini & George Kapetanios, 2004. "A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data," Working Papers 506, Queen Mary University of London, School of Economics and Finance.
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- Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
- 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.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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