國立虎尾科技大學 |

Data Reduction and Forecasting Techniques in Finance.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data Reduction and Forecasting Techniques in Finance./
Author:	Deiss, Theresia.
Description:	1 online resource (52 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Applied mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379544973

Data Reduction and Forecasting Techniques in Finance.
Deiss, Theresia.

Data Reduction and Forecasting Techniques in Finance. - 1 online resource (52 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--San Diego State University, 2023.

Includes bibliographical references

Forecasting interest rates has become a more and more relevant and involved topic in recent years, as interest rates are considered a key financial variable. This master thesis introduces a forecasting approach for a multi-dimensional interest rates data set. The forecasting method consists of two steps. First, a dimension-reduction technique called Principal Component Analysis (PCA) is applied, and, second, the resulting time series is forecasted using two different time series models, namely the ARIMA model and the GARCH model. The results are evaluated regarding in-sample and out-of-sample performance. Particularly, we consider possible model restrictions and study the effectiveness from an implementation point of view.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379544973Subjects--Topical Terms:

1069907
Applied mathematics.
Subjects--Index Terms:

Data reductionIndex Terms--Genre/Form:

554714
Electronic books.

Data Reduction and Forecasting Techniques in Finance.
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500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Curtis, Christopher W.
502 $a Thesis (M.S.)--San Diego State University, 2023.
504 $a Includes bibliographical references
520 $a Forecasting interest rates has become a more and more relevant and involved topic in recent years, as interest rates are considered a key financial variable. This master thesis introduces a forecasting approach for a multi-dimensional interest rates data set. The forecasting method consists of two steps. First, a dimension-reduction technique called Principal Component Analysis (PCA) is applied, and, second, the resulting time series is forecasted using two different time series models, namely the ARIMA model and the GARCH model. The results are evaluated regarding in-sample and out-of-sample performance. Particularly, we consider possible model restrictions and study the effectiveness from an implementation point of view.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Applied mathematics. $3 1069907
650 4 $a Statistics. $3 556824
650 4 $a Finance. $3 559073
653 $a Data reduction
653 $a Forecasting method
653 $a Principal Component Analysis
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0508
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a San Diego State University. $b Applied Mathematics. $3 1182520
773 0 $t Masters Abstracts International $g 84-11.
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