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

  • Tseng, Chih-Hsiung
  • Cheng, Sheng-Tzong
  • Wang, Yi-Hsien
  • Peng, Jin-Tang
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    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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    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
    DOI: 10.1016/j.physa.2008.01.074
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