Journal of Computational Finance

Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions

Cornelis W. Oosterlee, Qian Feng, Shashi Jain, Patrik Karlsson and Drona Kandhai

  • We provide algorithms for computing exposure distributions at any future time of Bermudan swaptions.
  • Robust, efficient and accurate simulation-based methods are provided for risk-neutral valuation within observed real-world scenarios.
  • The algorithms avoid the costly nested Monte Carlo simulation.
  • The algorithm based on Stochastic Grid Bundling Method is highly accurate particularly for computing potential future exposure profiles.


This paper presents a computationally efficient technique for the computation of exposure distributions at any future time under the risk-neutral and some observed real-world probability measures; these are needed for the computation of credit valuation adjustment (CVA) and potential future exposure (PFE). In particular,we present a valuation framework for Bermudan swaptions. The essential idea is to approximate the required value function via a set of risk-neutral scenarios and use this approximated value function on the set of observed real-world scenarios. This technique significantly improves the computational efficiency by avoiding nested Monte Carlo simulation and using only basic methods such as regression.We demonstrate the benefits of this technique by computing exposure distributions for Bermudan swaptions under the Hull-White and G2++ models.

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