Option pricing with the control variate technique beyond Monte Carlo simulation

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

The North American Journal of Economics and Finance, 2022, vol. 62, issue C

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.

Keywords: Numerical algorithm; Monte Carlo simulation; Control variate; Binomial tree; Convolution (search for similar items in EconPapers)
JEL-codes: C00 G13 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:62:y:2022:i:c:s1062940822001140

DOI: 10.1016/j.najef.2022.101772

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