Adjoint Algorithmic Differentiation

The ability to compute values and sensitivities of financial derivatives is essential to the financial institution for both risk management and regulatory purposes. However, the computation of the sensitivities can be difficult in terms of effort and time required. Adjoint algorithmic differentiation (AAD) is a mathematical technique that enhances performance and speed compared with the traditional 'bump and re-price' method. 

Felix Grevy, Director, Product Management and Tat Fung, Sr. Product Manager, Financial Engineering and Quantitative Finance, in collaboration with University College London, explain how Misys has managed to combine the benefit of graphic processing units (GPU) and AAD to achieve fast greeks computation. 

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