style="font-size:15px;"> Furmann, E., and R. Zitikis. 2008. Weighted premium calculation principles. Insurance, Mathematics and Economics, 459–465.
Girardi, G., and T. Ergün. 2012. Systemic risk measurement: Multivariate GARCH estimation of CoVaR. available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1783958.
Gray, D. F., A. A. Jobst, and S. W. Malone. 2010. Quantifying systemic risk and reconceptualizing the role of finance for economic growth. Journal of Investment Management 8(2).
Gray, D., and A. A. Jobst. 2010. New directions in financial sector and sovereign risk management. Journal of Investment Management 8(1).
Group of Ten. 2001. The G10 Report on Consolidation in the Financial Sector, Chap. 3, http://www.imf.org/external/np/g10/2001/01/Eng/pdf/file3.pdf
Guerra, S. M., B. M. Tabak, R. A. Penaloza, and R. C. de Castro. 2013. Systemic Risk Measures. Working paper 321, Banco do Brasil, http://www.bcb.gov.br/pec/wps/ingl/wps321.pdf [Online].
Hansen, L. P. 2012. Challenges in identifying and measuring systemic risk, s.l.: National Bureau of Economic Research.
Huang, X., H. Zhou, and H. Zhu. 2009. A framework for assessing the systemic risk of major financial institutions. Journal of Banking and Finance 33: 2036–2049.
Jin, X., and F. Nadal de Simone. 2013. Banking Systemic Vulnerabilities: A Tail-Risk Dynamic CIMDO Approach. Banque centrale de Luxembourg.
Kaufmann, G. G., and K. E. Scott. 2003. What is systemic risk, and do bank regulators retard or contribute to it? Independent Review 7: 371–391.
Kovacevic, R., and G. Ch. Pflug. 2014. Measuring and Managing Risk. Chapter 2 In: Investment Risk Management, edited by K. Baker and G. Filbeck, Oxford University Press, Oxford, UK.
Mainink, G., and E. Schaaning. 2014. On dependence consistency of CoVaR and some other systemic risk measures. Statistics and Risk Modeling.
Merton, R. C. 2009. On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. In: Continuous Time Finance (first published 1992). Wiley-Blackwell Publishing, New York, 388–412.
Segoviano Basurto, M. A. 2006. Consistent information multivariate density optimizing methodology, s.l.: London School of Economics, Discussion Paper 557.
Segoviano, M. A., and C. Goodhart. 2009. Banking Stability Measures. Washington, D.C.: International Monetary Fund.
Servigny, O. D., and O. Renault. 2007. Measuring and Managing Credit Risk. New York: McGraw-Hill.
Vasicek, O. 1987. Probability of loss on loan portfolios. K.M.V. Corporation 12(6).
_________. 1991. Limiting loan loss distribution. K.M.V. Corporation.
_________. 2002. Loan portfolio value. Risk, available at www.risk.net. December: 160–162.
Chapter 2
Supervisory Requirements and Expectations for Portfolio-Level Counterparty Credit Risk Measurement and Management
Introduction
A bank's counterparty credit risk (CCR) exposure quantifies how much money the counterparty might owe the bank in the event of default. The CCR quantity is broken down into current exposure (CE), which measures the exposure if the counterparty were to default today, and potential exposure (PE), which measures the potential increase in exposure that could occur between today and some time horizon in the future.
The time of default is typically modeled as a stochastic stopping time. As opposed to the known CE, the PE must be estimated, usually by simulation. First, the expected positive exposure (EPE) is computed by simulating a large number (on the order of 102 to 103) of different paths for the various underlying future prices in the possible market environments, using a so-called regularized variance-covariance matrix. Then the system prices each of the derivative transactions on each path for each sample date,3 computes collateral call amounts based on relevant marked-to-market (MTM) calculations, applies the portfolio effects of netting and collateral, and aggregates exposure results to compute the average exposure along a term structure.
While an EPE may be a good indicator of the cost to replace a contract should the counterparty default, EPE is not helpful in the trade inception approval process because of its volatility and the need for a high confidence interval. Therefore, many banks will also report a very high percentile (e.g., 97.7th or 97.5th) of the exposure distribution over a large number of paths.
Note that these peaks in exposure profiles are not simply added over different products for a given counterparty, as these peaks may happen at different points in time. Rather, the time profiles of exposures are summed over products traded with a single counterparty, and the peak of that time profile is the summary PE measure. This methodology is conservative, as PEs are simply added over counterparties, while the bank may enter trades that mitigate each other in terms of PE with different counterparties.
We can readily see that CCR measurement necessarily combines the tools of standard market risk measurement with the tools of standard credit risk determination, a unique challenge to both. This frequently requires calculating probability-of-default (PD), loss-given-default (LGD), exposure-at-default (EAD), and a credit rating of the counterparty.4
The credit valuation adjustment (CVA) is defined as the product of the EPE times the LGD times the cumulative mortality rate (CMR), where the CMR is simply a multi-period PD rate. This is structurally equivalent to pricing EPE as the contingent leg of a credit default swap (CDS) by applying the counterparty spread to it. Such a spread is either a market quote if the name has a bespoke traded CDS, or a pseudo-CDS spread computed along a grid arrayed by region, industry, rating, and tenor. In the worst case, bond or loan spreads are used, giving rise to basis risk. It can be recognized that it is this part of the process that joins the market and the credit risk aspects of the algorithm. Practices for measuring market risk are used in mapping derivatives exposures to a set of market risk factors (e.g., spreads, volatilities, or correlations), simulating those factors out to a forward-looking time horizon, and determining the distribution of the level of exposures over various realizations of these risk factors in the simulation. Separately, standard credit risk processes provide assessments of the credit quality of the counterparty, such as PD and LGD estimation.
Direct or originating businesses (i.e., trading desks) are viewed as credit portfolios: As their positions get in the money, this gives rise to CCR, since the counterparty may default while owing money to the bank. The CVA represents a daily MTM transfer price of default risk charged to the originating business for insuring default risk, which is the price of a pseudo-CDS hedge with the EPE as underlying notional. The group (e.g., the market risk management department) that sells insurance to the business at inception of the trade will cover any loss due to counterparty default. As the exposure rises, due to either an increase in the position or
Typical sample dates are: daily for the first two weeks, once a week out to a quarter, once a month out to a year, once a quarter out to 10 years, and once a year up to 50 years.
See Araten and Jacobs 2001; Araten, Jacobs, and Varshney 2004; Araten, Jacobs, Varshney, and Pellegrino 2004; Carey and Gordy 2004; Carey and Hrycay 2001; Frye and Jacobs 2012; Jacobs 2010a, b; Jacobs and Kiefer 2010; and Jacobs, Karagozoglu, and Layish 2012.
