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.

A stochastic variance factor model for large datasets and an application to S&P data / Cipollini, Andrea; Kapetanios, G.. - In: ECONOMICS LETTERS. - ISSN 0165-1765. - STAMPA. - 100:1(2008), pp. 130-134. [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

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
100
1
130
134
A stochastic variance factor model for large datasets and an application to S&P data / Cipollini, Andrea; Kapetanios, G.. - In: ECONOMICS LETTERS. - ISSN 0165-1765. - STAMPA. - 100:1(2008), pp. 130-134. [10.1016/j.econlet.2007.12.014]
Cipollini, Andrea; Kapetanios, G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/608705
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