A Comprehensive Method for Integrated (Realized) Volatility Estimation

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dc.contributor.advisorFu, Wenjiang
dc.contributor.committeeMemberPeters, Charles
dc.contributor.committeeMemberJi, Shanyu
dc.contributor.committeeMemberYang, Yipeng
dc.creatorZhang, Peixin 1986-
dc.date.accessioned2018-03-12T18:49:31Z
dc.date.available2018-03-12T18:49:31Z
dc.date.createdDecember 2017
dc.date.issued2017-12
dc.date.submittedDecember 2017
dc.date.updated2018-03-12T18:49:31Z
dc.description.abstractIn this dissertation, a comprehensive kernel-based estimator, PCA Kernel (PK), is studied to estimate the integrated (realized) volatility under the effect of a more realistic and complex market micro-structure noise. Challenging situations, such as irregular diurnal sampling times, moving-average (MA) long-memory noises, inhomogeneous latent log-price processes, jumps, diurnal pattern trading volumes, and non-linear trading information related noises, are considered to test the performance of the proposed estimator. The Principal Components Analysis (PCA) approach is implemented to improve the estimator's stability, and the linear, polynomial, and Gaussian kernels are applied in the reproducing kernel Hilbert spaces to test their de-noise capacities. The kernel-based estimator dramatically improves the efficiency and accuracy of volatility estimation in terms of both the empirical variance and bias.
dc.description.departmentMathematics, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/2878
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectPCA Kernel
dc.subjectIntegrated (realized) volatility
dc.subjectThe reproducing kernel Hilbert spaces
dc.titleA Comprehensive Method for Integrated (Realized) Volatility Estimation
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2019-12-01
local.embargo.terms2019-12-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentMathematics, Department of
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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