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

e-Archivo Repository

Show simple item record

dc.contributor.author Ausín Olivera, María Concepción
dc.contributor.author Galeano, Pedro
dc.contributor.author Ghosh, Pulak
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2010-09-21T12:27:22Z
dc.date.accessioned 2010-09-22T08:23:26Z
dc.date.available 2010-09-22T08:23:26Z
dc.date.issued 2010-09
dc.identifier.uri http://hdl.handle.net/10016/9283
dc.description.abstract Financial 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.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 10-22
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Bayesian estimation
dc.subject.other Deviance information criterion
dc.subject.other Dirichlet process mixture
dc.subject.other Financial time series
dc.subject.other Location-scale Gaussian mixture
dc.subject.other Markov chain Monte Carlo
dc.title A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws103822
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record