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 ap...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
MDPI AG
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/71135 |
| _version_ | 1848762398778851328 |
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| author | Luong, C. Dokuchaev, Nikolai |
| author_facet | Luong, C. Dokuchaev, Nikolai |
| author_sort | Luong, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:46:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-71135 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:46:56Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-711352019-01-15T03:04:14Z Forecasting of Realised Volatility with the Random Forests Algorithm Luong, C. Dokuchaev, Nikolai 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/71135 10.3390/jrfm11040061 http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Luong, C. Dokuchaev, Nikolai Forecasting of Realised Volatility with the Random Forests Algorithm |
| title | Forecasting of Realised Volatility with the Random Forests Algorithm |
| title_full | Forecasting of Realised Volatility with the Random Forests Algorithm |
| title_fullStr | Forecasting of Realised Volatility with the Random Forests Algorithm |
| title_full_unstemmed | Forecasting of Realised Volatility with the Random Forests Algorithm |
| title_short | Forecasting of Realised Volatility with the Random Forests Algorithm |
| title_sort | forecasting of realised volatility with the random forests algorithm |
| url | http://hdl.handle.net/20.500.11937/71135 |