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dc.contributor.authorLux, Thomasen_US
dc.date.accessioned2012-08-03en_US
dc.date.accessioned2012-08-10T16:54:40Z-
dc.date.available2012-08-10T16:54:40Z-
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/10419/60335-
dc.description.abstractMaximum likelihood estimation of discretely observed diffusion processes is mostly hampered by the lack of a closed form solution of the transient density. It has recently been argued that a most generic remedy to this problem is the numerical solution of the pertinent Fokker-Planck (FP) or forward Kol- mogorov equation. Here we expand extant work on univariate diffusions to higher dimensions. We find that in the bivariate and trivariate cases, a numerical solution of the FP equation via alternating direction finite difference schemes yields results surprisingly close to exact maximum likelihood in a number of test cases. After providing evidence for the effciency of such a numerical approach, we illustrate its application for the estimation of a joint system of short-run and medium run investor sentiment and asset price dynamics using German stock market data.en_US
dc.language.isoengen_US
dc.publisherKiel Institute for the World Economy (IfW) Kielen_US
dc.relation.ispartofseriesKiel Working Paper 1781en_US
dc.subject.jelC58en_US
dc.subject.jelG12en_US
dc.subject.jelC13en_US
dc.subject.ddc330en_US
dc.subject.keywordstochastic differential equationsen_US
dc.subject.keywordnumerical maximum likelihooden_US
dc.subject.keywordFokker-Planck equationen_US
dc.subject.keywordfinite difference schemesen_US
dc.subject.keywordasset pricingen_US
dc.subject.stwMaximum-Likelihood-Methodeen_US
dc.subject.stwAnalysisen_US
dc.subject.stwStochastischer Prozessen_US
dc.subject.stwTheorieen_US
dc.subject.stwBörsenkursen_US
dc.subject.stwAnlageverhaltenen_US
dc.subject.stwSchätzungen_US
dc.subject.stwDeutschlanden_US
dc.titleInference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approachen_US
dc.typeWorking Paperen_US
dc.identifier.ppn720581907en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
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