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Not so Particular about Calibration: Smile Problem Resolved

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  • Aitor Muguruza

Abstract

We present a novel Monte Carlo based LSV calibration algorithm that applies to all stochastic volatility models, including the non-Markovian rough volatility family. Our framework overcomes the limitations of the particle method proposed by Guyon and Henry-Labord\`ere (2012) and theoretically guarantees a variance reduction without additional computational complexity. Specifically, we obtain a closed-form and exact calibration method that allows us to remove the dependency on both the kernel function and bandwidth parameter. This makes the algorithm more robust and less prone to errors or instabilities in a production environment. We test the efficiency of our algorithm on various hybrid (rough) local stochastic volatility models.

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  • Aitor Muguruza, 2019. "Not so Particular about Calibration: Smile Problem Resolved," Papers 1909.13366, arXiv.org.
    • Handle: RePEc:arx:papers:1909.13366
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    1. Elisa Alòs & Jorge León & Josep Vives, 2007. "On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility," Finance and Stochastics, Springer, vol. 11(4), pages 571-589, October.
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    Cited by:

    1. Enrico Dall’Acqua & Riccardo Longoni & Andrea Pallavicini, 2023. "Rough-Heston Local-Volatility Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 26(06n07), pages 1-18, November.

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