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Inference for cumulative intraday return curves

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dc.contributor.authorZheng, Ben, author
dc.contributor.authorKokoszka, Piotr S., advisor
dc.contributor.authorCooley, Dan, committee member
dc.contributor.authorMiao, Hong, committee member
dc.contributor.authorZhou, Wen, committee member
dc.date.accessioned2019-01-07T17:19:15Z
dc.date.available2019-01-07T17:19:15Z
dc.date.issued2018
dc.description.abstractThe central theme of this dissertation is inference for cumulative intraday return (CIDR) curves computed from high frequency data. Such curves describe how the return on an investment evolves with time over a relatively short period. We introduce a functional factor model to investigate the dependence of cumulative return curves of individual assets on the market and other factors. We propose a new statistical test to determine whether this dependence is the same in two sample periods. The statistical power of the new test is validated by asymptotic theory and a simulation study. We apply this test to study the impact on individual stocks and Sector Exchanged-Traded Funds (ETF) of the recent financial crisis and of trends in the oil price. Our analysis reveals that the functional approach has an information content different from that obtained from scalar factor models for point-to-point returns. Motivated by the risk inherent in intraday investing, we propose several ways of quantifying extremal behavior of a time series of curves. A curve can be extreme if it has shape and/or magnitude much different than the bulk of observed curves. Our approach is at the nexus of Functional Data Analysis and Extreme Value Theory. The risk measures we propose allow us to assess probabilities of observing extreme curves not seen in a historical record. These measures complement risk measures based on point-to-point returns, but have different interpretation and information content. Using our approach, we study how the financial crisis of 2008 impacted the extreme behavior of intraday cumulative return curves. We discover different impacts on shares in important sectors of the US economy. The information our analysis provides is in some cases different from the conclusions based on the extreme value analysis of daily closing price returns. In a different direction, we investigate a large-scale multiple testing problem motivated by a biological study. We introduce mixed models to fit the longitudinal data and incorporate a bootstrap method to construct a false discovery rate (FDR) controlling procedure. A simulation study is implemented to show its effectiveness.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierZheng_colostate_0053A_15149.pdf
dc.identifier.urihttps://hdl.handle.net/10217/193124
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectextreme value theory
dc.subjectlarge-scale multiple testing
dc.subjecttwo sample test
dc.subjectfunctional data analysis
dc.subjectcumulative intraday return curves
dc.subjectrisk analysis
dc.titleInference for cumulative intraday return curves
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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