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A LONG-MEMORY VERSION OF THE BERGOMI MODEL: PRICING AND CALIBRATION FOR AMERICAN PUT OPTION

Arezou Karimi & Farshid Mehrdoust

International Journal of Theoretical and Applied Finance2025https://doi.org/10.1142/s0219024925500219article
AJG 2ABDC B
Weight
0.50

What the paper says

This paper extends the Bergomi variance model to a fractional model incorporating long-range memory, thereby enhancing its ability to capture intricate market dynamics, particularly volatility clustering. The existence and stability of the solution for the proposed variance curve are rigorously investigated. Subsequently, a combined formula for pricing American put options under the proposed model is derived, offering a computationally efficient alternative to conventional numerical techniques such as the least squares Monte Carlo algorithm (LSM) and the binomial tree methods. For model calibration, the interior point method (IPM) and sequential quadratic programming (SQP) algorithms are employed. Simulation results validate the effectiveness of the proposed model in replicating market volatility. Furthermore, the pricing formula for American put options, calibrated using IPM, demonstrates superior out-of-sample accuracy compared not only to SQP but also to the Black–Scholes model, highlighting the proposed model’s enhanced capability in capturing market-consistent prices.

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https://doi.org/https://doi.org/10.1142/s0219024925500219

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@article{arezou2025,
  title        = {{A LONG-MEMORY VERSION OF THE BERGOMI MODEL: PRICING AND CALIBRATION FOR AMERICAN PUT OPTION}},
  author       = {Arezou Karimi & Farshid Mehrdoust},
  journal      = {International Journal of Theoretical and Applied Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219024925500219},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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