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dc.contributor.authorFengler, Matthias R.en
dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorMammen, Ennoen
dc.date.accessioned2005-08-15-
dc.date.accessioned2009-07-23T14:42:53Z-
dc.date.available2009-07-23T14:42:53Z-
dc.date.issued2005-
dc.identifier.urihttp://hdl.handle.net/10419/25039-
dc.description.abstractA primary goal in modelling the implied volatility surface (IVS) for pricing and hedging aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This approach, however, neglects the degenerated string structure of the implied volatility data and may result in a modelling bias. We propose a dynamic semiparametric factor model (DSFM), which approximates the IVS in a finite dimensional function space. The key feature is that we only fit in the local neighborhood of the design points. Our approach is a combination of methods from functional principal component analysis and backfitting techniques for additive models. The model is found to have an approximate 10% better performance than a sticky moneyness model. Finally, based on the DSFM, we devise a generalized vega-hedging strategy for exotic options that are priced in the local volatility framework. The generalized vega-hedging extends the usual approaches employed in the local volatility framework.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2005,020en
dc.subject.jelC14en
dc.subject.jelG12en
dc.subject.ddc330en
dc.subject.keywordsmileen
dc.subject.keywordlocal volatilityen
dc.subject.keywordgeneralized additive modelen
dc.subject.keywordbackfittingen
dc.subject.keywordfunctional principal component analysisen
dc.titleA dynamic semiparametric factor model for implied volatility string dynamics-
dc.type|aWorking Paperen
dc.identifier.ppn496022024en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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