the web's biggest credit risk modeling resource.

Credit Jobs

Home Glossary Links FAQ / About Site Guide Search


Submit Your Paper

In Rememberance: World Trade Center (WTC)

doi> search: A or B

Export citation to:
- Text (plain)
- BibTeX

Fast Correlation Greeks by Adjoint Algorithmic Differentiation

by Luca Capriotti of Credit Suisse Group AG, and
Mike Giles of the University of Oxford

April 13, 2010

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.

Keywords: Algorithmic Differentiation, Monte Carlo Simulations, Derivatives Pricing, CreditDerivatives.

Books Referenced in this Paper:  (what is this?)

Download paper (445K PDF) 6 pages