Fast correlation Greeks by adjoint algorithmic differentiation

Adjoint methods have recently been proposed as an efficient way to calculate risk through Monte Carlo simulation. Luca Capriotti and Mike Giles extend these ideas and show how adjoint algorithmic differentiation allows for fast calculation of price sensitivities in full generality. They illustrate the method for the calculation of correlation risk and test it numerically for portfolio default options

One of the consequences of the recent crisis of the financial markets is a renewed emphasis on rigorous risk management practices. To quantify the financial exposure of financial firms, and to ensure efficient capital allocation and more effective hedging practices, regulators and senior management alike are insisting more and more on a thorough monitoring of risk. Among all businesses, those dealing with complex, over-the-counter derivatives are the ones receiving the most attention.


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