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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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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 506.

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

Note: A revised version is available at the personal homepage of George Kapetanios.
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Related research
Keywords: Stochastic volatility Factor models Principal components

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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  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. [Downloadable!]
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This page was last updated on 2008-10-30.


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