Speed-up credit exposure calculations for pricing and risk management

TL;DR

This paper presents a novel, efficient method for calculating credit exposure of options that achieves high accuracy comparable to full re-evaluation but with faster computation, suitable for various models and asset classes.

Contribution

It introduces a dynamic programming-based function approximation method that provides closed-form exposure estimates, improving speed and accuracy over existing regression-based techniques.

Findings

Achieves comparable accuracy to full re-evaluation methods.

Faster than traditional regression-based approaches.

Validated on multiple equity products and interest rate derivatives.

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. Key advantage of 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. 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.