Could machine learning improve CVA and IM calculations?

Banks have built ways to calculate CVA more quickly, but neural networks could offer more accurate method

Credit valuation adjustments (CVA) and initial margin (IM) requirements aren’t the easiest measures to compute, especially for a large portfolio of assets.

Both require users to calculate the future exposure of the portfolio in question, which means the path of each individual asset needs to be simulated. All might have non-linear features such as embedded optionality and exercise rights, making it impossible to use simple partial differential equations (PDEs) – which become very difficult to

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