Dynamic semiparametric factor models in risk neutral density estimation - Enzo Giacomini; Wolfgang Karl Härdle; Volker Krätschmer (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 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: C14; C32; G12; 330; Dynamic factor models; dimension reduction; risk neutral density; Faktorenanalyse; Nichtparametrisches Verfahren; Dynamisches Modell; Theorie; Schätzung; Aktienoption; Risikoneutralität; Statistische Verteilung; Deutschland