Data analytics enhanced component volatility model

doi:10.1016/j.eswa.2017.05.025

Data analytics enhanced component volatility model

2017-10-01

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DOI：10.1016/j.eswa.2017.05.025

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Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. 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 filter, 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 volatility 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. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords:

Autoregressive neural network

Hybrid model

Two-component

Volatility model

Data analytics enhanced component volatility model

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