Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25280
Authors: 
Giacomini, Enzo
Härdle, Wolfgang Karl
Krätschmer, Volker
Year of Publication: 
2008
Series/Report no.: 
SFB 649 discussion paper 2008,038
Abstract: 
Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most prominent dimension reduction technique - functional principal components analysis - however, does not model time dependences embedded in functional data. In this paper we use dynamic semiparametric factor models (DSFM) to reduce dimensionality and analyse the dynamic structure of unknown random functions by means of inference based on their lower dimensional representation. We apply DSFM to estimate the dynamic structure of risk neutral densities implied by prices of option on the DAX stock index.
Subjects: 
Dynamic factor models
dimension reduction
risk neutral density
JEL: 
C14
C32
G12
Document Type: 
Working Paper

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