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.

Kapetanios, G., Cipollini, A. (2008). A stochastic variance factor model for large datasets and an application to S&P data. ECONOMICS LETTERS, 100(1), 130-135 [10.1016/j.econlet.2007.12.014].

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

CIPOLLINI, Andrea
2008-01-01

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.
2008
Kapetanios, G., Cipollini, A. (2008). A stochastic variance factor model for large datasets and an application to S&P data. ECONOMICS LETTERS, 100(1), 130-135 [10.1016/j.econlet.2007.12.014].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/101437
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