SFB 649 Discussion Paper 2008-038
19 Pages Posted: 9 Jan 2017
Date Written: May 19, 2008
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.
Keywords: dynamic factor models, dimension reduction, risk neutral density
JEL Classification: C14, C32, G12
Suggested Citation: Suggested Citation
Giacomini, Enzo and Härdle, Wolfgang K. and Kratschmer, Volker, Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation (May 19, 2008). SFB 649 Discussion Paper 2008-038. Available at SSRN: https://ssrn.com/abstract=2894283