Option pricing with the control variate technique beyond Monte Carlo simulation

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number101772
JournalNorth American Journal of Economics and Finance
Volume62
DOIs
StatePublished - Nov 2022

Keywords

  • Binomial tree
  • Control variate
  • Convolution
  • Monte Carlo simulation
  • Numerical algorithm

Fingerprint

Dive into the research topics of 'Option pricing with the control variate technique beyond Monte Carlo simulation'. Together they form a unique fingerprint.

Cite this