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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

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.

Suggested Citation

  • Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    • 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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    14. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
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