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Implied volatility forecast and option trading strategy

Author

Listed:
  • Liu, Dehong
  • Liang, Yucong
  • Zhang, Lili
  • Lung, Peter
  • Ullah, Rizwan

Abstract

We examine the implied volatility derived from an improved Artificial Bee Colony with Back Propagation (BP) neural network model that is Artificial Bee Colony-Back Propagation (ABC-BP) neural network model. We find that the improved model can better predict the implied volatility than basic BP neural network model and Monte Carlo simulation. Nevertheless, the option price derived from the Monte Carlo simulation is more efficient when we apply the simulation to the option straddle trading strategy. Additionally, in a robustness test we find that our proposed neural network model performs better than the traditional GARCH model in building up option trading strategies.

Suggested Citation

  • Liu, Dehong & Liang, Yucong & Zhang, Lili & Lung, Peter & Ullah, Rizwan, 2021. "Implied volatility forecast and option trading strategy," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 943-954.
  • Handle: RePEc:eee:reveco:v:71:y:2021:i:c:p:943-954
    DOI: 10.1016/j.iref.2020.10.023
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    References listed on IDEAS

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    Cited by:

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    2. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.

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    More about this item

    Keywords

    ABC-BP neural network model; Implied volatility; Options trading strategy;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • G1 - Financial Economics - - General Financial Markets

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