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...

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Main Authors:	Chuong Luong, Nikolai Dokuchaev
Format:	Article
Language:	English
Published:	MDPI AG 2018-10-01
Series:	Journal of Risk and Financial Management
Subjects:	realised volatility heterogeneous autoregressive model purified implied volatility classification random forests machine learning
Online Access:	http://www.mdpi.com/1911-8074/11/4/61

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spelling	doaj-99a913ee41114d45b37e7c41a9b7bf572020-11-24T21:58:58ZengMDPI AGJournal of Risk and Financial Management1911-80742018-10-011146110.3390/jrfm11040061jrfm11040061Forecasting of Realised Volatility with the Random Forests AlgorithmChuong Luong0Nikolai Dokuchaev1School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, AustraliaSchool of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, AustraliaThe 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.http://www.mdpi.com/1911-8074/11/4/61realised volatilityheterogeneous autoregressive modelpurified implied volatilityclassificationrandom forestsmachine learning
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Chuong Luong Nikolai Dokuchaev
spellingShingle	Chuong Luong Nikolai Dokuchaev Forecasting of Realised Volatility with the Random Forests Algorithm Journal of Risk and Financial Management realised volatility heterogeneous autoregressive model purified implied volatility classification random forests machine learning
author_facet	Chuong Luong Nikolai Dokuchaev
author_sort	Chuong Luong
title	Forecasting of Realised Volatility with the Random Forests Algorithm
title_short	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_sort	forecasting of realised volatility with the random forests algorithm
publisher	MDPI AG
series	Journal of Risk and Financial Management
issn	1911-8074
publishDate	2018-10-01
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.
topic	realised volatility heterogeneous autoregressive model purified implied volatility classification random forests machine learning
url	http://www.mdpi.com/1911-8074/11/4/61
work_keys_str_mv	AT chuongluong forecastingofrealisedvolatilitywiththerandomforestsalgorithm AT nikolaidokuchaev forecastingofrealisedvolatilitywiththerandomforestsalgorithm
_version_	1725849938282151936

Forecasting of Realised Volatility with the Random Forests Algorithm

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