Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte-Carlo Method

25 Pages Posted: 19 Dec 2014

See all articles by Cornelis de Graaf

Cornelis de Graaf

University of Amsterdam

Drona Kandhai

University of Amsterdam; ING Bank - Netherlands Office

Peter Sloot

University of Amsterdam; St. Petersburg National Research University ITMO; Nanyang Technological University (NTU)

Multiple version iconThere are 2 versions of this paper

Date Written: November 10, 2014

Abstract

According to Basel III, financial institutions have to charge a Credit Valuation Adjustment (CVA) to account for a possible counterparty default. Calculating this measure and its sensitivities is one of the big challenges in risk management. Here we introduce an efficient method for the estimation of CVA and its sensitivities for a portfolio of financial derivatives. We use the Finite Difference Monte-Carlo (FDMC) method to measure exposure profiles and consider the computationally challenging case of FX barrier options in the context of the Black-Scholes as well as the Heston Stochastic Volatility model for a wide range of parameters. Our results show that FDMC is an accurate method compared to the semi-analytic COS method and has as an advantage that it can compute multiple options on one grid, which paves the way for real portfolio level risk analysis.

Keywords: Expected Exposure, CVA, Potential Future Exposure, sensitivities, barrier options, Heston, numerical computation, finite differences, Portfolio

JEL Classification: C63, G12

Suggested Citation

de Graaf, Cornelis and Kandhai, Drona and Sloot, Peter, Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte-Carlo Method (November 10, 2014). Available at SSRN: https://ssrn.com/abstract=2521431 or http://dx.doi.org/10.2139/ssrn.2521431

Cornelis De Graaf (Contact Author)

University of Amsterdam ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

Drona Kandhai

University of Amsterdam ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

ING Bank - Netherlands Office

1102 MG Amsterdam
P.O. Box 1800
1000 BV Amsterdam
Netherlands

Peter Sloot

University of Amsterdam ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

St. Petersburg National Research University ITMO

Kronverkskiy pr., 49
Saint Petersburg, 197101
Russia

Nanyang Technological University (NTU)

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

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