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You searched for +publisher:"Colorado State University" +contributor:("Miao, Hong"). Showing records 1 – 2 of 2 total matches.

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Colorado State University

1. Zheng, Ben. Inference for cumulative intraday return curves.

Degree: PhD, Statistics, 2018, Colorado State University

The 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. Advisors/Committee Members: Kokoszka, Piotr S. (advisor), Cooley, Dan (committee member), Miao, Hong (committee member), Zhou, Wen (committee member).

Subjects/Keywords: extreme value theory; large-scale multiple testing; two sample test; functional data analysis; cumulative intraday return curves; risk analysis

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APA (6th Edition):

Zheng, B. (2018). Inference for cumulative intraday return curves. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/193124

Chicago Manual of Style (16th Edition):

Zheng, Ben. “Inference for cumulative intraday return curves.” 2018. Doctoral Dissertation, Colorado State University. Accessed December 03, 2020. http://hdl.handle.net/10217/193124.

MLA Handbook (7th Edition):

Zheng, Ben. “Inference for cumulative intraday return curves.” 2018. Web. 03 Dec 2020.

Vancouver:

Zheng B. Inference for cumulative intraday return curves. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2020 Dec 03]. Available from: http://hdl.handle.net/10217/193124.

Council of Science Editors:

Zheng B. Inference for cumulative intraday return curves. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/193124


Colorado State University

2. Young, Gabriel J. Inference for functional time series with applications to yield curves and intraday cumulative returns.

Degree: PhD, Statistics, 2016, Colorado State University

Econometric and financial data often take the form of a functional time series. Examples include yield curves, intraday price curves and term structure curves. Before an attempt is made to statistically model or predict such series, we must address whether or not such a series can be assumed stationary or trend stationary. We develop extensions of the KPSS stationarity test to functional time series. Motivated by the problem of a change in the mean structure of yield curves, we also introduce several change point methods applied to dynamic factor models. For all testing procedures, we include a complete asymptotic theory, a simulation study, illustrative data examples, as well as details of the numerical implementation of the testing procedures. The impact of scheduled macroeconomic announcements has been shown to account for sizable fractions of total annual realized stock returns. To assess this impact, we develop methods of derivative estimation which utilize a functional analogue of local-polynomial smoothing. The confidence bands are then used to find time intervals of statistically increasing cumulative returns. Advisors/Committee Members: Kokoszka, Piotr S. (advisor), Miao, Hong (committee member), Breidt, F. Jay (committee member), Zhou, Wen (committee member).

Subjects/Keywords: factor models; time series; yield curves; functional data; change point; trend stationarity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Young, G. J. (2016). Inference for functional time series with applications to yield curves and intraday cumulative returns. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/173395

Chicago Manual of Style (16th Edition):

Young, Gabriel J. “Inference for functional time series with applications to yield curves and intraday cumulative returns.” 2016. Doctoral Dissertation, Colorado State University. Accessed December 03, 2020. http://hdl.handle.net/10217/173395.

MLA Handbook (7th Edition):

Young, Gabriel J. “Inference for functional time series with applications to yield curves and intraday cumulative returns.” 2016. Web. 03 Dec 2020.

Vancouver:

Young GJ. Inference for functional time series with applications to yield curves and intraday cumulative returns. [Internet] [Doctoral dissertation]. Colorado State University; 2016. [cited 2020 Dec 03]. Available from: http://hdl.handle.net/10217/173395.

Council of Science Editors:

Young GJ. Inference for functional time series with applications to yield curves and intraday cumulative returns. [Doctoral Dissertation]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/173395

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