Artikel
A multivariate Kernel approach to forecasting the variance covariance of stock market returns
This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. The model makes use of similarity forecasting techniques and it is demonstrated that several popular techniques can be thought as a subset of this approach. A forecasting experiment demonstrates the potential for the technique to improve the statistical accuracy of forecasts of variance-covariance matrices.
- Sprache
-
Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 6 ; Year: 2018 ; Issue: 1 ; Pages: 1-27 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Forecasting Models; Simulation Methods
Financial Econometrics
- Thema
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volatility forecasting
kernel density estimation
similarity forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Becker, Ralf
Clements, Adam
O'Neill, Robert
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2018
- DOI
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doi:10.3390/econometrics6010007
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Becker, Ralf
- Clements, Adam
- O'Neill, Robert
- MDPI
Entstanden
- 2018
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