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A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAusín Olivera, María Concepción
dc.contributor.authorGaleano, Pedro
dc.contributor.authorGhosh, Pulak
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2010-09-21T12:27:22Z
dc.date.accessioned2010-09-22T08:23:26Z
dc.date.available2010-09-22T08:23:26Z
dc.date.issued2010-09
dc.description.abstractFinancial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution, leading to a Dirichlet process mixture model. The proposed specification allows for a greater exibility in capturing both the skewness and kurtosis frequently observed in financial returns. The Bayesian model provides statistical inference with finite sample validity. Furthermore, it is also possible to obtain predictive distributions for the Value at Risk (VaR), which has become the most widely used measure of market risk for practitioners. Through a simulation study, we demonstrate the performance of the proposed semiparametric method and compare results with the ones from a normal distribution assumption. We also demonstrate the superiority of our proposed semiparametric method using real data from the Bombay Stock Exchange Index (BSE-30) and the Hang Seng Index (HSI).
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.identifier.repecws103822
dc.identifier.urihttps://hdl.handle.net/10016/9283
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries10-22
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherBayesian estimation
dc.subject.otherDeviance information criterion
dc.subject.otherDirichlet process mixture
dc.subject.otherFinancial time series
dc.subject.otherLocation-scale Gaussian mixture
dc.subject.otherMarkov chain Monte Carlo
dc.titleA semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation
dc.typeworking paper*
dspace.entity.typePublication
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