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dc.contributor.advisor Cox, Dennis D.
dc.creatorEgbulefu, Joseph
dc.date.accessioned 2014-08-26T18:55:38Z
dc.date.available 2014-08-26T18:55:38Z
dc.date.created 2014-05
dc.date.issued 2014-04-24
dc.date.submitted May 2014
dc.identifier.citation Egbulefu, Joseph. "Robust GARCH methods and analysis of partial least squares regression." (2014) Diss., Rice University. https://hdl.handle.net/1911/76713.
dc.identifier.urihttps://hdl.handle.net/1911/76713
dc.description.abstract New 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectVolatility forecasting
Partial least squares
GARCH
Asymptotic covariance
Seasonal volatility
Variable selection
dc.title Robust GARCH methods and analysis of partial least squares regression
dc.contributor.committeeMember Ensor, Katherine B.
dc.contributor.committeeMember El-Gamal, Mahmoud A.
dc.date.updated 2014-08-26T18:55:39Z
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy


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