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J. Risk Financial Manag. 2018, 11(4), 61; https://doi.org/10.3390/jrfm11040061

Forecasting of Realised Volatility with the Random Forests Algorithm

School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia
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Received: 8 September 2018 / Revised: 8 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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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. View Full-Text
Keywords: realised volatility; heterogeneous autoregressive model; purified implied volatility; classification; random forests; machine learning realised volatility; heterogeneous autoregressive model; purified implied volatility; classification; random forests; machine learning
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Luong, C.; Dokuchaev, N. Forecasting of Realised Volatility with the Random Forests Algorithm. J. Risk Financial Manag. 2018, 11, 61.

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