Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction

  • Altaf Hossain
  • Faisal Zaman
  • M. Nasser
  • M. Mufakhkharul Islam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

This article applied GARCH model instead AR or ARMA model to compare with the standard BP and SVM in forecasting of the four international including two Asian stock markets indices.These models were evaluated on five performance metrics or criteria. Our experimental results showed the superiority of SVM and GARCH models, compared to the standard BP in forecasting of the four international stock markets indices.

Index Terms

Generalized Autoregressive Conditional Heteroskedastic (GARCH) Neural Network (NN) Back Propagation (BP) Artificial BPNN (BPANN) Support Vector Machine(SVM) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Altaf Hossain
    • 1
  • Faisal Zaman
    • 2
  • M. Nasser
    • 1
  • M. Mufakhkharul Islam
    • 3
  1. 1.Department of StatisticsRajshahi UniversityRajshahiBangladesh
  2. 2.Department of System Design and InformaticsKyushu Institute of TechnologyFukukaJapan
  3. 3.Department of Computer Science & EngineeringBUETDhakaBangladesh

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