No. 506: 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

February 1, 2004

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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.

J.E.L classification codes: C32, C33, G12

Keywords:Stochastic volatility, Factor models, Principal components