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

Andrea Cipollini and George Kapetanios

No 506, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Stochastic volatility; Factor models; Principal components (search for similar items in EconPapers)
JEL-codes: C32 C33 G12 (search for similar items in EconPapers)
Date: 2004-02-01
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Citations: View citations in EconPapers (3)

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Journal Article: A stochastic variance factor model for large datasets and an application to S&P data (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:506

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