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Existence of a calibrated regime switching local volatility model

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  • Benjamin Jourdain
  • Alexandre Zhou

Abstract

By Gyöngy's theorem, a local and stochastic volatility model is calibrated to the market prices of all European call options with positive maturities and strikes if its local volatility (LV) function is equal to the ratio of the Dupire LV function over the root conditional mean square of the stochastic volatility factor given the spot value. This leads to a stochastic differential equation (SDE) nonlinear in the sense of McKean. Particle methods based on a kernel approximation of the conditional expectation, as presented in Guyon and Henry‐Labordère [Risk Magazine, 25, 92–97], provide an efficient calibration procedure even if some calibration errors may appear when the range of the stochastic volatility factor is very large. But so far, no global existence result is available for the SDE nonlinear in the sense of McKean. When the stochastic volatility factor is a jump process taking finitely many values and with jump intensities depending on the spot level, we prove existence of a solution to the associated Fokker–Planck equation under the condition that the range of the squared stochastic volatility factor is not too large. We then deduce existence to the calibrated model by extending the results in Figalli [Journal of Functional Analysis, 254(1), 109–153].

Suggested Citation

  • Benjamin Jourdain & Alexandre Zhou, 2020. "Existence of a calibrated regime switching local volatility model," Mathematical Finance, Wiley Blackwell, vol. 30(2), pages 501-546, April.
    • Handle: RePEc:bla:mathfi:v:30:y:2020:i:2:p:501-546
    DOI: 10.1111/mafi.12231
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    Citations

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    Cited by:

    1. Christian Bayer & Denis Belomestny & Oleg Butkovsky & John Schoenmakers, 2022. "A Reproducing Kernel Hilbert Space approach to singular local stochastic volatility McKean-Vlasov models," Papers 2203.01160, arXiv.org, revised Jan 2024.
    2. Martin Larsson & Shukun Long, 2024. "Markovian projections for It\^o semimartingales with jumps," Papers 2403.15980, arXiv.org.
    3. Mathias Beiglbock & George Lowther & Gudmund Pammer & Walter Schachermayer, 2021. "Faking Brownian motion with continuous Markov martingales," Papers 2109.12927, arXiv.org.
    4. Mathias Beiglbock & Gudmund Pammer & Walter Schachermayer, 2021. "From Bachelier to Dupire via Optimal Transport," Papers 2106.12395, arXiv.org.
    5. Scander Mustapha, 2024. "Strong existence and uniqueness of a calibrated local stochastic volatility model," Papers 2406.14074, arXiv.org.
    6. Ivan Guo & Grégoire Loeper & Shiyi Wang, 2022. "Calibration of local‐stochastic volatility models by optimal transport," Mathematical Finance, Wiley Blackwell, vol. 32(1), pages 46-77, January.
    7. Mathias Beiglböck & Gudmund Pammer & Walter Schachermayer, 2022. "From Bachelier to Dupire via optimal transport," Finance and Stochastics, Springer, vol. 26(1), pages 59-84, January.
    8. Christoph Reisinger & Maria Olympia Tsianni, 2023. "Convergence of the Euler--Maruyama particle scheme for a regularised McKean--Vlasov equation arising from the calibration of local-stochastic volatility models," Papers 2302.00434, arXiv.org, revised Aug 2023.

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