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

Karlsson, Patrik LU ; Feng, Qian; Jain, Shashi; Kandhai, Drona and Oosterlee, Cornelis W. (2016) In Journal of Computational Finance 20(1). p.139-172
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
credit valuation adjustment (CVA), bermudan options, potential future exposure models (PFE)
in
Journal of Computational Finance
volume
20
issue
1
pages
34 pages
publisher
Incisive Media Ltd.
external identifiers
  • scopus:84993944722
ISSN
1460-1559
DOI
10.21314/JCF.2017.337
language
English
LU publication?
yes
id
e412637c-7f51-4215-b641-b4c08de48ef0
date added to LUP
2016-10-19 21:12:25
date last changed
2017-02-22 08:22:51
@article{e412637c-7f51-4215-b641-b4c08de48ef0,
  abstract     = {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.},
  author       = {Karlsson, Patrik and Feng, Qian and Jain, Shashi and Kandhai, Drona and Oosterlee, Cornelis W.},
  issn         = {1460-1559},
  keyword      = {credit valuation adjustment (CVA),bermudan options,potential future exposure models (PFE)},
  language     = {eng},
  month        = {07},
  number       = {1},
  pages        = {139--172},
  publisher    = {Incisive Media Ltd.},
  series       = {Journal of Computational Finance},
  title        = {Efficient computation of exposure profiles on real-world and risk-neutral scenarios for Bermudan swaptions},
  url          = {http://dx.doi.org/10.21314/JCF.2017.337},
  volume       = {20},
  year         = {2016},
}