Forecasting of Realised Volatility with the Random Forests Algorithm
Chuong Luong () and
Nikolai Dokuchaev ()
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Chuong Luong: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia
Nikolai Dokuchaev: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia
Journal of Risk and Financial Management, 2018, vol. 11, issue 4, 1-15
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.
Keywords: realised volatility; heterogeneous autoregressive model; purified implied volatility; classification; random forests; machine learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:61-:d:175017
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