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Empirical of the Taiwan stock index option price forecasting model - applied artificial neural network

Author

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  • Chin-Tsai Lin
  • Hsin-Yi Yeh

Abstract

This work presents a novel neural network model for forecasting option prices using past volatilities and other options market factors. Out of different approaches to estimating volatility in the option pricing model, this study uses backpropagation neural network to forecast prices for Taiwanese stock index options. The ability to develop accurate forecasts of grey prediction volatility enables practitioners to establish an appropriate hedging strategy at in-the-money option.

Suggested Citation

  • Chin-Tsai Lin & Hsin-Yi Yeh, 2009. "Empirical of the Taiwan stock index option price forecasting model - applied artificial neural network," Applied Economics, Taylor & Francis Journals, vol. 41(15), pages 1965-1972.
    • Handle: RePEc:taf:applec:v:41:y:2009:i:15:p:1965-1972
    DOI: 10.1080/00036840601131672
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    Cited by:

    1. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    2. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    3. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    4. Chuang Yuang Lin & Dar Hsin Chen & Chin Yu Tsai, 2011. "The limitation of monotonicity property of option prices: an empirical evidence," Applied Economics, Taylor & Francis Journals, vol. 43(23), pages 3103-3113.

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