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General lattice methods for arithmetic Asian options

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  • Gambaro, Anna Maria
  • Kyriakou, Ioannis
  • Fusai, Gianluca

Abstract

In this research, we develop a new discrete-time model approach with flexibly changeable driving dynamics for pricing Asian options, with possible early exercise, and a fixed or floating strike price. These options are ubiquitous in financial markets but can also be recast in the framework of real options. Moreover, we derive an accurate lower bound to the price of the European Asian options under stochastic volatility. We also survey theoretical aspects; more specifically, we prove that our tree method for the European Asian option in the binomial model is unconditionally convergent to the continuous-time equivalent. Numerical experiments confirm smooth, monotonic convergence, highly precise performance, and robustness with respect to changing driving dynamics and contract features.

Suggested Citation

  • Gambaro, Anna Maria & Kyriakou, Ioannis & Fusai, Gianluca, 2020. "General lattice methods for arithmetic Asian options," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1185-1199.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:3:p:1185-1199
    DOI: 10.1016/j.ejor.2019.10.026
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    Cited by:

    1. Alghalith, Moawia, 2019. "The distribution of the average of log-normal variables and exact Pricing of the Arithmetic Asian Options: A Simple, closed-form Formula," MPRA Paper 105588, University Library of Munich, Germany.
    2. Weinan Zhang & Pingping Zeng, 2023. "A transform-based method for pricing Asian options under general two-dimensional models," Quantitative Finance, Taylor & Francis Journals, vol. 23(11), pages 1677-1697, November.
    3. Alghalith, Moawia, 2019. "A New Price of the Arithmetic Asian Option: A Simple Formula," MPRA Paper 117047, University Library of Munich, Germany.
    4. Kahalé, Nabil, 2020. "General multilevel Monte Carlo methods for pricing discretely monitored Asian options," European Journal of Operational Research, Elsevier, vol. 287(2), pages 739-748.
    5. Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2021. "Moving average options: Machine Learning and Gauss-Hermite quadrature for a double non-Markovian problem," Papers 2108.11141, arXiv.org.
    6. Goudenège, Ludovic & Molent, Andrea & Zanette, Antonino, 2022. "Moving average options: Machine learning and Gauss-Hermite quadrature for a double non-Markovian problem," European Journal of Operational Research, Elsevier, vol. 303(2), pages 958-974.

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