Forecasting of Realised Volatility with the Random Forests Algorithm

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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Bibliographic Details
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
Description
Summary: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.
ISSN:1911-8074

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