Robust GARCH methods and analysis of partial least squares regression

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dc.contributor.advisorCox, Dennis D.en_US
dc.contributor.committeeMemberEnsor, Katherine B.en_US
dc.contributor.committeeMemberEl-Gamal, Mahmoud A.en_US
dc.creatorEgbulefu, Josephen_US
dc.date.accessioned2014-08-26T18:55:38Zen_US
dc.date.available2014-08-26T18:55:38Zen_US
dc.date.created2014-05en_US
dc.date.issued2014-04-24en_US
dc.date.submittedMay 2014en_US
dc.date.updated2014-08-26T18:55:39Zen_US
dc.description.abstractNew approaches to modeling volatility are evaluated and properties of partial least squares (PLS) regression are investigated. Common methods for modeling volatility, the standard deviation of price changes over a period, that account for the heavy tails of asset returns rely on maximum likelihood estimation using a heavy-tailed distribu- tion. A fractional power GARCH model is developed for robust volatility modeling of heavy tailed returns using a fractional power transform and Gaussian quasi maximum likelihood estimation. Furthermore, a smooth periodic GARCH model, incorporating seasonal trends by wavelet analysis, is developed and shown to outperform existing approaches in long-horizon volatility forecasting. PLS is a latent variable method for regression with correlated predictors. Previous approaches to derive the asymptotic covariance of PLS regression coefficients rely on restrictive assumptions. The asymptotic covariance of PLS coefficients are derived under general conditions. PLS regression is applied to variable selection in the context of index tracking.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationEgbulefu, Joseph. "Robust GARCH methods and analysis of partial least squares regression." (2014) Diss., Rice University. <a href="https://hdl.handle.net/1911/76713">https://hdl.handle.net/1911/76713</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/76713en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectVolatility forecastingen_US
dc.subjectPartial least squaresen_US
dc.subjectGARCHen_US
dc.subjectAsymptotic covarianceen_US
dc.subjectSeasonal volatilityen_US
dc.subjectVariable selectionen_US
dc.titleRobust GARCH methods and analysis of partial least squares regressionen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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