2016-09-01
Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions
Publication
Publication
Journal of Computational Finance , Volume 20 - Issue 1 p. 139- 172
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.
Additional Metadata | |
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Risk Publications | |
ING Bank, Amsterdam, The Netherlands | |
doi.org/10.21314/JCF.2017.337 | |
Journal of Computational Finance | |
Organisation | Scientific Computing |
Oosterlee, K., Feng, Q., Jain, S., Karlsson, P., & Kandhai, B. D. (2016). Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions. Journal of Computational Finance, 20(1), 139–172. doi:10.21314/JCF.2017.337 |