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Data Reduction and Forecasting Techniques in Finance.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Data Reduction and Forecasting Techniques in Finance./
作者:
Deiss, Theresia.
面頁冊數:
1 online resource (52 pages)
附註:
Source: Masters Abstracts International, Volume: 84-11.
Contained By:
Masters Abstracts International84-11.
標題:
Finance. -
電子資源:
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:
559073
Finance.
Subjects--Index Terms:
Data reductionIndex Terms--Genre/Form:
554714
Electronic books.
Data Reduction and Forecasting Techniques in Finance.
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Advisor: Curtis, Christopher W.
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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.
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click for full text (PQDT)
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