Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/8920
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dc.contributor.authorYu, Y-
dc.contributor.authorYu, K-
dc.contributor.authorWang, H-
dc.contributor.authorLi, M-
dc.date.accessioned2014-08-19T10:30:09Z-
dc.date.available2014-08-19T10:30:09Z-
dc.date.issued2009-
dc.identifier.citationStatistica Sinica, 19(2), 843 - 867, 2009en_US
dc.identifier.issn1017-0405-
dc.identifier.urihttp://www3.stat.sinica.edu.tw/statistica/j19n2/19-2.htmlen
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/8920-
dc.descriptionCopyright @ 2009 Institute of Statistical Science, Academia Sinicaen_US
dc.description.abstractWe develop two likelihood-based approaches to semiparametrically estimate a class of time-inhomogeneous diffusion processes: log penalized splines (P-splines) and the local log-linear method. Positive volatility is naturally embedded and this positivity is not guaranteed in most existing diffusion models. We investigate different smoothing parameter selections. Separate bandwidths are used for drift and volatility estimation. In the log P-splines approach, different smoothness for different time varying coefficients is feasible by assigning different penalty parameters. We also provide theorems for both approaches and report statistical inference results. Finally, we present a case study using the weekly three-month Treasury bill data from 1954 to 2004. We find that the log P-splines approach seems to capture the volatility dip in mid-1960s the best. We also present an application to calculate a financial market risk measure called Value at Risk (VaR) using statistical estimates from log P-splines.en_US
dc.language.isoenen_US
dc.publisherInstitute of Statistical Science, Academia Sinica & International Chinese Statistical Associationen_US
dc.subjectBandwidth selectionen_US
dc.subjectKernel smoothingen_US
dc.subjectLocal linearen_US
dc.subjectOption pricingen_US
dc.subjectPenalized likelihooden_US
dc.subjectVaRen_US
dc.subjectVariance estimationen_US
dc.subjectVolatilityen_US
dc.titleSemiparametric estimation for a class of time-inhomogenous diffusion processesen_US
dc.typeArticleen_US
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Appears in Collections:Dept of Mathematics Research Papers
Mathematical Sciences

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