Artikel
Improving the accuracy: Volatility modeling and forecasting using high-frequency data and the variational component
In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic "heterogeneous autoregressive" (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Industrial Engineering and Management (JIEM) ; ISSN: 2013-0953 ; Volume: 3 ; Year: 2010 ; Issue: 1 ; Pages: 199-220 ; Barcelona: OmniaScience
- Classification
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Management
- Subject
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realized volatility
forecasting
time series analysis
autoregressive model
- Event
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Geistige Schöpfung
- (who)
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Kumar, Manish
- Event
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Veröffentlichung
- (who)
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OmniaScience
- (where)
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Barcelona
- (when)
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2010
- DOI
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doi:10.3926/jiem.v3n1.p199-220
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Kumar, Manish
- OmniaScience
Time of origin
- 2010
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