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dc.contributor.authorLuong, C.
dc.contributor.authorDokuchaev, Nikolai
dc.identifier.citationLuong, C. and Dokuchaev, N. 2018. Forecasting of Realised Volatility with the Random Forests Algorithm. Journal of Risk and Financial Management. 11 (4): Article ID 61.

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.

dc.publisherMDPI AG
dc.titleForecasting of Realised Volatility with the Random Forests Algorithm
dc.typeJournal Article
dcterms.source.titleJournal of Risk and Financial Management
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusOpen access

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