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Forecasting stock volatility process using improved least square support vector machine approach

  • Xiao-Li Gong
  • Xi-Hua LiuEmail author
  • Xiong Xiong
  • Xin-Tian Zhuang
Methodologies and Application
  • 45 Downloads

Abstract

Considering that the stock returns distribution displays leptokurtosis as well as left-skewed properties, and the returns volatility process exhibits heteroscedasticity as well as clustering effects, the asymmetric GARCH-type models with non-Gaussian distributions (AGARCH-nG) are employed to describe the volatility process. In addition, the AGARCH-nG models are hybridized with artificial neural network (ANN) technique for forecasting stock returns volatility. Since the least square support vector machine (LS-SVM) technique displays strong forecast ability, we present an improved particle swarm optimization (IPSO) algorithm to optimize the parameters of LS-SVM technique in the process of stock returns volatility prediction. Then, we compare the forecasting performances of individual AGARCH-nG models, the hybrid AGARCH-nG-ANN methods and the data mining-based LS-SVM-IPSO method using stock markets data. The empirical results verify the effectiveness and superiority of the proposed method, which demonstrates that the LS-SVM-IPSO approach outperforms the AGARCH-type models with non-Gaussian distributions and those integrating with the artificial neural network methods.

Keywords

Stock volatility forecasting Leptokurtosis distribution Artificial neural network Least square support vector machine Particle swarm optimization algorithm 

Notes

Acknowledgements

We would like to acknowledge the financial support from the National Social Science Foundation of China (No. 18BGL200), the National Natural Science Foundation of China (No. 71532009), Research funding of Qingdao University (No. 41118010080).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animals rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiao-Li Gong
    • 1
  • Xi-Hua Liu
    • 1
    Email author
  • Xiong Xiong
    • 2
    • 3
  • Xin-Tian Zhuang
    • 4
  1. 1.School of EconomicsQingdao UniversityQingdaoChina
  2. 2.College of Management and EconomicsTianjin UniversityTianjinChina
  3. 3.China Center for Social Computing and AnalyticsTianjinChina
  4. 4.School of Business AdministrationNortheastern UniversityShenyangChina

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