dc.contributor.advisor | Munk, Axel Prof. Dr. | |
dc.contributor.author | Sabel, Till | |
dc.date.accessioned | 2014-07-28T09:33:27Z | |
dc.date.available | 2014-07-28T09:33:27Z | |
dc.date.issued | 2014-07-28 | |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-0022-5F35-2 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-4610 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-4610 | |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | |
dc.subject.ddc | 510 | de |
dc.title | Simultaneous Confidence Statements about the Diffusion Coefficient of an Ito-Process with Application to Spot Volatility Estimation | de |
dc.type | doctoralThesis | de |
dc.contributor.referee | Munk, Axel Prof. Dr. | |
dc.date.examination | 2014-07-16 | |
dc.description.abstracteng | In this PhD thesis, we address the problem of giving simultaneous confidence statements about local features of the diffusion of an Itô process. To this end, we construct a multiscale test based on weighted quadratic variation and prove that the test statistic can be strongly approximated by a sequence of Gaussian martingales which are distribution-free. Further, we give optimality results and present different visualization methods.
In the second part of the thesis, we extend the approach to data corrupted by additive noise to cover applications from high-frequency finance. Additionally, we show which difficulties arise from real data and apply our method exemplarily to prices of Euro-Bund-Futures (FGBL).
As an outlook for future work, we present ideas of generalizing the method to inference on the local covariance and point out some interesting applications from finance. | de |
dc.contributor.coReferee | Dümbgen, Lutz Prof. Dr. | |
dc.subject.eng | nonparametric statistics; multiscale testing; volatility estimation | de |
dc.identifier.urn | urn:nbn:de:gbv:7-11858/00-1735-0000-0022-5F35-2-4 | |
dc.affiliation.institute | Fakultät für Mathematik und Informatik | de |
dc.subject.gokfull | Mathematics (PPN61756535X) | de |
dc.identifier.ppn | 791336395 | |