6533b7d5fe1ef96bd126535f
RESEARCH PRODUCT
A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
Andrea CipolliniGeorge Kapetaniossubject
Economics and EconometricsMultivariate statisticsPrincipal componentsStochastic volatilityjel:C32jel:C33jel:G12Factor modelPrincipal component analysisEconometricsEconomicsStochastic volatility Factor models Principal componentsStochastic volatilityforecasting; stochastic volatility; large datasetFinanceFactor analysisdescription
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.
year | journal | country | edition | language |
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2008-01-01 |