A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
Economics Letters, Vol. 100, pp. 130–134, 2008
Posted: 16 May 2009
Date Written: July 1, 2008
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.
Keywords: Stochastic volatility, Factor models, Principal components
JEL Classification: C32, C33, G12
Suggested Citation: Suggested Citation