
 Fast Correlation Greeks by Adjoint Algorithmic Differentiation by Luca Capriotti of Credit Suisse Group AG, and 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 copulabased 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?) 