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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Inference for functional time series with applications to yield curves and intraday cumulative returns./
作者:	Young, Gabriel J.
面頁冊數:	1 online resource (176 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (176 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339936833Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Inference for functional time series with applications to yield curves and intraday cumulative returns.
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500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Piotr S. Kokoszka.
502 $a Thesis (Ph.D.)--Colorado State University, 2016.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Finance. $3 559073
650 4 $a Economics. $3 555568
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0508
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710 2 $a Colorado State University. $b Statistics. $3 1148437
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