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Exact and high order discretization schemes for Wishart processes and their affine extensions

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  • Abdelkoddousse Ahdida

    (CERMICS)

  • Aur'elien Alfonsi

    (CERMICS)

Abstract

This work deals with the simulation of Wishart processes and affine diffusions on positive semidefinite matrices. To do so, we focus on the splitting of the infinitesimal generator, in order to use composition techniques as Ninomiya and Victoir or Alfonsi. Doing so, we have found a remarkable splitting for Wishart processes that enables us to sample exactly Wishart distributions, without any restriction on the parameters. It is related but extends existing exact simulation methods based on Bartlett's decomposition. Moreover, we can construct high-order discretization schemes for Wishart processes and second-order schemes for general affine diffusions. These schemes are in practice faster than the exact simulation to sample entire paths. Numerical results on their convergence are given.

Suggested Citation

  • Abdelkoddousse Ahdida & Aur'elien Alfonsi, 2010. "Exact and high order discretization schemes for Wishart processes and their affine extensions," Papers 1006.2281, arXiv.org, revised Mar 2013.
    • Handle: RePEc:arx:papers:1006.2281
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    References listed on IDEAS

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    1. W. B. Smith & R. R. Hocking, 1972. "Wishart Variate Generator," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(3), pages 341-345, November.
    2. Syoiti Ninomiya & Nicolas Victoir, 2008. "Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(2), pages 107-121.
    3. Martino Grasselli & Claudio Tebaldi, 2008. "Solvable Affine Term Structure Models," Mathematical Finance, Wiley Blackwell, vol. 18(1), pages 135-153, January.
    4. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
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    Cited by:

    1. Gaetano Bua & Daniele Marazzina, 2021. "On the application of Wishart process to the pricing of equity derivatives: the multi-asset case," Computational Management Science, Springer, vol. 18(2), pages 149-176, June.
    2. Aur'elien Alfonsi & David Krief & Peter Tankov, 2018. "Long-time large deviations for the multi-asset Wishart stochastic volatility model and option pricing," Papers 1806.06883, arXiv.org.
    3. Alfonsi, Aurélien & Kebaier, Ahmed & Rey, Clément, 2016. "Maximum likelihood estimation for Wishart processes," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3243-3282.
    4. Chulmin Kang & Wanmo Kang, 2013. "Exact Simulation of Wishart Multidimensional Stochastic Volatility Model," Papers 1309.0557, arXiv.org.
    5. Christa Cuchiero & Claudio Fontana & Alessandro Gnoatto, 2019. "Affine multiple yield curve models," Mathematical Finance, Wiley Blackwell, vol. 29(2), pages 568-611, April.
    6. Alessandro Gnoatto & Martino Grasselli, 2011. "The explicit Laplace transform for the Wishart process," Papers 1107.2748, arXiv.org, revised Aug 2013.
    7. Chulmin Kang & Wanmo Kang & Jong Mun Lee, 2017. "Exact Simulation of the Wishart Multidimensional Stochastic Volatility Model," Operations Research, INFORMS, vol. 65(5), pages 1190-1206, October.
    8. Gaetano La Bua & Daniele Marazzina, 2022. "A new class of multidimensional Wishart-based hybrid models," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 209-239, June.
    9. Abdelkoddousse Ahdida & Aur'elien Alfonsi & Ernesto Palidda, 2014. "Smile with the Gaussian term structure model," Papers 1412.7412, arXiv.org, revised Nov 2015.

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