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Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing

Author

Listed:
  • Efe Arin

    (TOBB University of Economics and Technology)

  • A. Murat Ozbayoglu

    (TOBB University of Economics and Technology)

Abstract

Options are commonly used by traders and investors for hedging their investments. They also allow the traders to execute leveraged trading opportunities. Meanwhile accurately pricing the intended option is crucial to perform such tasks. The most common technique used in options pricing is Black–Scholes (BS) formula. However, there are slight differences between the BS model output and the actual options price due to the ambiguity in defining the volatility. In this study, we developed hybrid deep learning based options pricing models to achieve better pricing compared to BS. The results indicate that the proposed models can generate more accurate prices for all option classes. Compared with BS using annualized 20 intraday returns as volatility, 94.5% improvement is achieved in option pricing in terms of mean squared error.

Suggested Citation

  • Efe Arin & A. Murat Ozbayoglu, 2022. "Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 39-58, January.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10063-9
    DOI: 10.1007/s10614-020-10063-9
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    References listed on IDEAS

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    Cited by:

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    2. Peiwei Cao & Xubiao He, 2024. "Machine Learning Solutions for Fast Real Estate Derivatives Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2003-2032, October.

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