A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
AbstractThe 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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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 506.
Date of creation: Feb 2004
Date of revision:
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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Stochastic volatility; Factor models; Principal components;
Other versions of this item:
- Cipollini, A. & Kapetanios, G., 2008. "A stochastic variance factor model for large datasets and an application to S&P data," Economics Letters, Elsevier, vol. 100(1), pages 130-134, July.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-02-15 (All new papers)
- NEP-CFN-2004-02-15 (Corporate Finance)
- NEP-ECM-2004-02-20 (Econometrics)
- NEP-FIN-2004-02-15 (Finance)
- NEP-FMK-2004-02-15 (Financial Markets)
- NEP-RMG-2004-02-15 (Risk Management)
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- 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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