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Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

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  • Tseng, Chih-Hsiung
  • Cheng, Sheng-Tzong
  • Wang, Yi-Hsien
  • Peng, Jin-Tang
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    Abstract

    This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108000320
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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 387 (2008)
    Issue (Month): 13 ()
    Pages: 3192-3200

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    Handle: RePEc:eee:phsmap:v:387:y:2008:i:13:p:3192-3200

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Artificial neural networks; EGARCH; Grey forecasting model;

    References

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    Cited by:
    1. Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.

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