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postgraduate thesis: Modeling of high-dimensional realized volatility matrices with financial applications

TitleModeling of high-dimensional realized volatility matrices with financial applications
Authors
Advisors
Advisor(s):Li, WKYao, JJ
Issue Date2017
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Shen, K. [沈可仁]. (2017). Modeling of high-dimensional realized volatility matrices with financial applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractWith the availability of ultra-high-frequency data, it is possible to construct and model realized volatility and co-volatility nowadays and they have caught great attention. There are mainly three issues that should be considered when realized volatility/co-volatility is constructed, which is microstructure noise associated with high-frequency data, the asynchronicity of different assets and the high-dimensionality of the covariance matrix. In this thesis, the three issues are discussed and the high-dimensionality is mainly focused on. In Chapter 2, a brief literature review of the construction and modeling of realized volatility and co-volatility is provided. In Chapter 3, a spiked model for the high-dimensional realized covariance matrix is proposed which deals with the estimation of the spiked eigenvalues of the integrated covariance matrix when the dimension of the matrix is large. The proposed estimator is proved to be consistent and its nice empirical performance is presented. In addition, a factor Conditional Autoregressive Wishart model is proposed in Chapter 4 to model the high-dimensional realized covariance matrices. The proposed model reduces the number of parameters needed, ensures the positive-definiteness of the covariance matrix, and maintains a competitive forecasting ability compared to the existing model. Furthermore, the two higher order realized measures, namely realized skewness and kurtosis, are studied for their empirical properties in Chapter 5. It is found that the realized kurtosis has nice explanatory ability for the future volatility, confirmed by extensive data analysis.
DegreeDoctor of Philosophy
SubjectMultivariate analysis
Mathematical models - Finance
Dept/ProgramStatistics and Actuarial Science
Persistent Identifierhttp://hdl.handle.net/10722/249911

 

DC FieldValueLanguage
dc.contributor.advisorLi, WK-
dc.contributor.advisorYao, JJ-
dc.contributor.authorShen, Keren-
dc.contributor.author沈可仁-
dc.date.accessioned2017-12-19T09:27:44Z-
dc.date.available2017-12-19T09:27:44Z-
dc.date.issued2017-
dc.identifier.citationShen, K. [沈可仁]. (2017). Modeling of high-dimensional realized volatility matrices with financial applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/249911-
dc.description.abstractWith the availability of ultra-high-frequency data, it is possible to construct and model realized volatility and co-volatility nowadays and they have caught great attention. There are mainly three issues that should be considered when realized volatility/co-volatility is constructed, which is microstructure noise associated with high-frequency data, the asynchronicity of different assets and the high-dimensionality of the covariance matrix. In this thesis, the three issues are discussed and the high-dimensionality is mainly focused on. In Chapter 2, a brief literature review of the construction and modeling of realized volatility and co-volatility is provided. In Chapter 3, a spiked model for the high-dimensional realized covariance matrix is proposed which deals with the estimation of the spiked eigenvalues of the integrated covariance matrix when the dimension of the matrix is large. The proposed estimator is proved to be consistent and its nice empirical performance is presented. In addition, a factor Conditional Autoregressive Wishart model is proposed in Chapter 4 to model the high-dimensional realized covariance matrices. The proposed model reduces the number of parameters needed, ensures the positive-definiteness of the covariance matrix, and maintains a competitive forecasting ability compared to the existing model. Furthermore, the two higher order realized measures, namely realized skewness and kurtosis, are studied for their empirical properties in Chapter 5. It is found that the realized kurtosis has nice explanatory ability for the future volatility, confirmed by extensive data analysis.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshMultivariate analysis-
dc.subject.lcshMathematical models - Finance-
dc.titleModeling of high-dimensional realized volatility matrices with financial applications-
dc.typePG_Thesis-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineStatistics and Actuarial Science-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2017-
dc.identifier.mmsid991043976389603414-

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