Speed-up credit exposure calculations for pricing and risk management

Abstract

We introduce a new method to calculate the credit exposure of European and path-dependent options. The proposed method is able to calculate accurate expected exposure and potential future exposure profiles under the risk-neutral and the real-world measure. A key advantage is that it delivers an accuracy comparable to a full re-evaluation and at the same time it is faster than a regression-based method. The core of the approach is solving a dynamic programming problem by function approximation. This yields a closed-form approximation along the paths together with the option's delta and gamma. The simple structure allows for highly efficient evaluation of the exposures, even for a large number of simulated paths. The approach is flexible in the model choice, payoff profiles and asset classes. We validate the accuracy of the method numerically for three different equity products and a Bermudan interest rate swaption. Benchmarking against the popular least-squares Monte Carlo approach shows that our method is able to deliver a higher accuracy in a faster runtime.