Artikel
Forecasting of realised volatility with the random forests algorithm
The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 11 ; Year: 2018 ; Issue: 4 ; Pages: 1-15 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
realised volatility
heterogeneous autoregressive model
purified implied volatility
classification
random forests
machine learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Luong, Chuong
Dokučaev, Nikolaj G.
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2018
- DOI
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doi:10.3390/jrfm11040061
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 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
- Luong, Chuong
- Dokučaev, Nikolaj G.
- MDPI
Entstanden
- 2018
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