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Inference for functional time series with applications to yield curves and intraday cumulative returns.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Inference for functional time series with applications to yield curves and intraday cumulative returns./
Author:	Young, Gabriel J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	176 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:	Dissertation Abstracts International77-11B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10138072
ISBN:	9781339936833

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

Inference for functional time series with applications to yield curves and intraday cumulative returns. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 176 p.

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--Colorado State University, 2016.

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.

ISBN: 9781339936833Subjects--Topical Terms:

556824
Statistics.

Inference for functional time series with applications to yield curves and intraday cumulative returns.
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300 $a 176 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
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502 $a Thesis (Ph.D.)--Colorado State University, 2016.
520 $a 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.
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