Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62869
Authors: 
Cipollini, Andrea
Kapetanios, George
Year of Publication: 
2004
Series/Report no.: 
Working Paper, Department of Economics, Queen Mary, University of London 506
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.
Subjects: 
Stochastic volatility, Factor models, Principal components
JEL: 
C32
C33
G12
Document Type: 
Working Paper

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