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Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

Author

Listed:
  • Capriotti, Luca

    (Quantitative Strategies, Investment Banking Division, Credit Suisse Group)

Abstract

We show how Adjoint Algorithmic Differentiation can be combined with the so-called Pathwise Derivative and Likelihood Ratio Method to construct efficient Monte Carlo estimators of second order price sensitivities of derivative portfolios. We demonstrate with a numerical example how the proposed technique can be straightforwardly implemented to greatly reduce the computation time of second order risk.

Suggested Citation

  • Capriotti, Luca, 2015. "Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 81-87.
    • Handle: RePEc:ris:iosalg:0038
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    Citations

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    Cited by:

    1. Roberto Daluiso & Giorgio Facchinetti, 2018. "Algorithmic Differentiation For Discontinuous Payoffs," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-41, June.
    2. Roberto Daluiso, 2023. "Fast and Stable Credit Gamma of CVA," Papers 2311.11672, arXiv.org.

    More about this item

    Keywords

    Adjoint Algorithmic Differentiation; Monte Carlo; derivatives securities; risk management;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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