Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation
AbstractDimension 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-038.
Length: 19 pages
Date of creation: May 2008
Date of revision:
dynamic factor models; dimension reduction; risk neutral density;
Other versions of this item:
- Enzo Giacomini & Wolfgang Härdle & Volker Krätschmer, 2009. "Dynamic semiparametric factor models in risk neutral density estimation," AStA Advances in Statistical Analysis, Springer, vol. 93(4), pages 387-402, December.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
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