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Data analytics enhanced component volatility model

Yao, Yuan, Zhai, Jia, Cao, Yi, Ding, Xuemei, Liu, Junxiu and Luo, Yuling (2017) Data analytics enhanced component volatility model Expert Systems with Applications, 84. pp. 232-241.

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Volatility modelling and forecasting have attracted many attentions in both finance and computation ar- eas. Recent advances in machine learning allow us to construct complex models on volatility forecast- ing. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass fil- ter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatil- ity across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Yao, Yuan
Zhai, Jia
Ding, Xuemei
Liu, Junxiu
Luo, Yuling
Date : 10 May 2017
DOI : 10.1016/j.eswa.2017.05.025
Copyright Disclaimer : © 2017 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : Autoregressive neural network; Hybrid model; Two-component; Volatility model
Depositing User : Clive Harris
Date Deposited : 11 Sep 2017 15:12
Last Modified : 20 Apr 2018 12:55

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