Back
 JFRM  Vol.6 No.3 , September 2017
Modeling and Quantifying of the Global Wrong Way Risk
Abstract: The counterparty risk issue has become increasingly important in the world of finance. This risk is defined as the loss due to the counterparty default. The regulator uses the Credit Value Adjustment (CVA) to measure this risk. However, there is the independency assumption between the default and the exposure behind the CVA computation and it is not verified on the financial market. This paper presents two mathematical models for the assessment and the quantification of the counterparty risk without this assumption. This kind of risk is known as Wrong Way Risk (WWR). This study focuses on three approaches: empirical, copula and mixed model. The first one is based on the hazard rate modelling to express the correlation between the probability of default and the exposure. The second one is about calculating the WWR effect using copulas. The last one is a combination of both. There is another assumption that makes easier the CVA computation: The constant of the loss given default (LGD). As we know this assumption is not verified because the LGD could be deterministic or stochastic. Otherwise, it could lead to a correlation effect between the LGD, the exposure and the default, and we then obtain a Global Wrong Way Risk (GWWR). Indeed, we propose a model allowing the CVA quantification without these assumptions.
Cite this paper: Slime, B. (2017) Modeling and Quantifying of the Global Wrong Way Risk. Journal of Financial Risk Management, 6, 231-246. doi: 10.4236/jfrm.2017.63017.
References

[1]   Basel Committee on Banking Supervision (2010). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems.
http://www.bis.org/publ/bcbs189_dec2010.pdf, December.

[2]   Bocker, K., & Brunnbauer, M. (2014). Path Consistent Wrong Way Risk. Risk Magazine, 27, 49-53.

[3]   Credit Suisse Financial Products (1997). Credit Risk+: A Credit Risk Management Framework. London: Credit Suisse Financial Products.

[4]   Daniel, W. W. (1990). Spearman Rank Correlation Coefficient. Applied Nonparametric Statistics (2nd ed., pp. 358-365). Boston, MA: PWS-Kent.

[5]   Fenton, L. (1960). The Sum of Lognormal Probability Distribution in Scatter Transmission System, IEEE Trans. Communication Systems, 8, 56-57.

[6]   Gourieroux, C., Laurent, J. P., & Scaillet O. (2000). Sensitivity Analysis of Values at Risk. Journal of Empirical Finance, 7, 225-245.

[7]   Heston, S. L., (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6, 327-343.

[8]   Hull, J., & White, A. (2012). CVA and Wrong Way Risk. Financial Analysis Journal, 68, 58-69.
https://doi.org/10.2469/faj.v68.n5.6

[9]   Rosen, D., & Saunders D. (2012). CVA the Wrong Way. Journal of Risk Management in Financial Institutions, 5, 252-272.

[10]   Slime, B. (2016). Credit Name Concentration Risk: Granularity Adjustment Approximation. Journal of Financial Risk Management, 5, 246-263.
https://doi.org/10.4236/jfrm.2016.54023

 
 
Top