A stochastic variance factor model for large datasets and an application to S&P data

Andrea Cipollini and George Kapetanios

Economics Letters, 2008, vol. 100, issue 1, 130-134

Abstract: The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest the use of the principal component methodology of Stock and Watson [Stock, J.H., Watson, M.W., 2002. Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147-162] for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard [Harvey, A.C., Ruiz, E., Shephard, N., 1994. Multivariate Stochastic Variance Models. Review of Economic Studies, 61, 247-264]. We provide theoretical and Monte Carlo results on this method and apply it to S&P data.

Date: 2008
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Working Paper: A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:100:y:2008:i:1:p:130-134

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