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Option pricing with the control variate technique beyond Monte Carlo simulation

Author

Listed:
  • Chiu, Chun-Yuan
  • Dai, Tian-Shyr
  • Lyuu, Yuh-Dauh
  • Liu, Liang-Chih
  • Chen, Yu-Ting

Abstract

Although mostly used alongside Monte Carlo simulation, the control-variate (CV) technique can be applied to other numerical algorithms in option pricing. This paper studies the conditions under which a numerical method (simulation-based or not) can benefit from the CV technique and what approximators can serve as CVs. We demonstrate the ideas with Carr and Madan’s Fourier transform-based algorithm, convolution-based pricing algorithms, and classic binomial trees. Numerical results are provided to show that the CV-enhanced versions are more efficient than the original algorithms.

Suggested Citation

  • Chiu, Chun-Yuan & Dai, Tian-Shyr & Lyuu, Yuh-Dauh & Liu, Liang-Chih & Chen, Yu-Ting, 2022. "Option pricing with the control variate technique beyond Monte Carlo simulation," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:ecofin:v:62:y:2022:i:c:s1062940822001140
    DOI: 10.1016/j.najef.2022.101772
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Numerical algorithm; Monte Carlo simulation; Control variate; Binomial tree; Convolution;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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