Data analytics enhanced component volatility model

Yuan Yao, Jia Zhai*, Yi Cao, Xuemei Ding, Junxiu Liu, Yuling Luo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)232-241
Number of pages10
JournalExpert Systems with Applications
Publication statusPublished - 30 Oct 2017
Externally publishedYes


  • Autoregressive neural network
  • Hybrid model
  • Two-component
  • Volatility model


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