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Capturing the volatility smile of options on high-tech stocks—A combined GARCH-neural network approach

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  • Gunter Meissner
  • Noriko Kawano

Abstract

A slight modification of the standard GARCH equation results in a good modeling of historical volatility. Using this generated GARCH volatility together with the inputs: spot price divided by strike, time to maturity, and interest rate, a generated Neural Network results in significantly better pricing performance than the Black Scholes model. A single Neural Network for each individual high-tech stock is able to adapt to the market inherent volatility distortion. A single Network for all tested high-tech stocks also results in significantly better pricing performance than the Black-Scholes model. Copyright Academy of Economics and Finance 2001

Suggested Citation

  • Gunter Meissner & Noriko Kawano, 2001. "Capturing the volatility smile of options on high-tech stocks—A combined GARCH-neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 25(3), pages 276-292, September.
    • Handle: RePEc:spr:jecfin:v:25:y:2001:i:3:p:276-292
    DOI: 10.1007/BF02745889
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    References listed on IDEAS

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    Cited by:

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    2. Mark T. Leung & An‐Sing Chen & Ruben Mancha, 2009. "Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(4), pages 257-277, October.
    3. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    4. Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
    5. Jaros{l}aw Gruszka & Janusz Szwabi'nski, 2022. "Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices," Papers 2211.14814, arXiv.org.
    6. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.

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