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dc.contributor.authorFabozzi, Frank
dc.contributor.authorFallahgoul, Hassan
dc.contributor.authorVeredas, David
dc.date.accessioned2017-12-02T15:00:42Z
dc.date.available2017-12-02T15:00:42Z
dc.date.issued2019
dc.identifier.doi10.2139/ssrn.2620621
dc.identifier.urihttp://hdl.handle.net/20.500.12127/5829
dc.description.abstractIf the closed-form formula for the probability density function is not available, implementing the maximum likelihood estimation is challenging. We introduce a simple, fast, and accurate way for the estimation of numerous distributions that belong to the class of tempered stable probability distributions. Estimation is based on the Method of Simulated Quantiles (Dominicy and Veredas (2013)). MSQ consists of matching empirical and theoretical functions of quantiles that are informative about the parameters of interest. In the Monte Carlo study we show that MSQ is significantly faster than Maximum Likelihood and the estimates are almost as precise as MLE. A Value at Risk study using 13 years of daily returns from 21 world-wide market indexes shows that MSQ estimates provide as good risk assessments as with MLE.
dc.language.isoen
dc.publisherSpringer
dc.subjectHeavy Tailed Distribution
dc.subjectTempered Stable Distribution
dc.subjectMethod of Simulated Quantiles
dc.titleQuantile-based inference for tempered stable distributions
dc.identifier.journalComputational Economics
dc.source.volume53
dc.source.issue1
dc.source.beginpage51
dc.source.endpage83
dc.identifier.eissn1572-9974
vlerick.knowledgedomainAccounting & Finance
vlerick.typearticleJournal article with impact factor
vlerick.vlerickdepartmentA&F
dc.identifier.vperid234881
dc.identifier.vperid234882
dc.identifier.vperid181874
dc.identifier.vpubid7143


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