Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238927 
Year of Publication: 
2018
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 11 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-15
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
realised volatility
heterogeneous autoregressive model
purified implied volatility
classification
random forests
machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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