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Fast Correlation Greeks by Adjoint Algorithmic Differentiation

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  • Luca Capriotti
  • Mike Giles
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    Abstract

    We show how Adjoint Algorithmic Differentiation (AAD) allows an extremely efficient calculation of correlation Risk of option prices computed with Monte Carlo simulations. A key point in the construction is the use of binning to simultaneously achieve computational efficiency and accurate confidence intervals. We illustrate the method for a copula-based Monte Carlo computation of claims written on a basket of underlying assets, and we test it numerically for Portfolio Default Options. For any number of underlying assets or names in a portfolio, the sensitivities of the option price with respect to all the pairwise correlations is obtained at a computational cost which is at most 4 times the cost of calculating the option value itself. For typical applications, this results in computational savings of several order of magnitudes with respect to standard methods.

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    File URL: http://arxiv.org/pdf/1004.1855
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1004.1855.

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    Date of creation: Apr 2010
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    Publication status: Published in Risk Magazine, April 2010
    Handle: RePEc:arx:papers:1004.1855

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    Web page: http://arxiv.org/

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    Cited by:
    1. Cristian Homescu, 2011. "Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance," Papers 1107.1831, arXiv.org.

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