Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/124181
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dc.titleNONPARAMETRIC ESTIMATION OF CONDITIONAL VARIANCE AND COVARIANCE FUNCTIONS
dc.contributor.authorJIANG HUI
dc.date.accessioned2016-05-31T18:01:08Z
dc.date.available2016-05-31T18:01:08Z
dc.date.issued2016-01-20
dc.identifier.citationJIANG HUI (2016-01-20). NONPARAMETRIC ESTIMATION OF CONDITIONAL VARIANCE AND COVARIANCE FUNCTIONS. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/124181
dc.description.abstractIn recent times, estimation of the conditional covariance matrix has received much attention in many areas. Even though several statistical models have been introduced to avoid the curse of dimensionality problem, those models still have limited capability to describe different patterns of dependence in the data. In this thesis, we study this estimation problem through two aspects. First, to study the correlation structure for a portfolio of financial assets, we explore the effect of the exogenous variable on pairwise correlations by utilizing a reduced rank model. The second problem considered is how to efficiently estimate conditional variance (covariance) functions. Instead of estimating the mean function at the first stage, we propose a novel approach by combining the techniques in kernel smoothing and difference-based method, which outperforms two existing approaches in most cases. Furthermore, we provide a detailed theoretical justification, including consistency and asymptotic normality of our proposed estimators.
dc.language.isoen
dc.subjectreduced rank model, cross difference method, local linear, smoothing, conditional variance, conditional correlation coefficient
dc.typeThesis
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.contributor.supervisorXIA YINGCUN
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

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