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Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation Author info | Abstract | Publisher info | Download info | Related research | Statistics Enzo Giacomini
Wolfgang Härdle
Volker Krätschmer
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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.
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number
SFB649DP2008-038.
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Length: 19 pages
Date of creation: May 2008Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-038Contact details of provider: Postal: Spandauer Str. 1,10178 Berlin Phone: +49-30-2093-5708 Fax: +49-30-2093-5617 Email: Web page: http://sfb649.wiwi.hu-berlin.de More information through EDIRC
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Keywords: dynamic factor models ; dimension reduction ; risk neutral density ; Other versions of this item:
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
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