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dc.contributor.authorFengler, Matthias R.en_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorMammen, Ennoen_US
dc.date.accessioned2005-08-15en_US
dc.date.accessioned2009-07-23T14:42:53Z-
dc.date.available2009-07-23T14:42:53Z-
dc.date.issued2005en_US
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_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2005,020en_US
dc.subject.jelC14en_US
dc.subject.jelG12en_US
dc.subject.ddc330en_US
dc.subject.keywordsmileen_US
dc.subject.keywordlocal volatilityen_US
dc.subject.keywordgeneralized additive modelen_US
dc.subject.keywordbackfittingen_US
dc.subject.keywordfunctional principal component analysisen_US
dc.titleA dynamic semiparametric factor model for implied volatility string dynamicsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn496022024en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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