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CCR is defined as the risk that the counterparty to a transaction could default or deteriorate in creditworthiness before the final settlement of a transaction's cash flows. Unlike a loan, where only a bank faces the risk of loss, CCR creates a bilateral risk of loss because the market value of a transaction can be positive or negative to either counterparty. The future market value of the exposure and the counterparty's credit quality are uncertain and may vary over time as underlying market factors change. The regulatory focus is on institutions with large derivatives portfolios setting their risk management practices as well as on supervisors as they assess and examine CCR management.

CCR is multidimensional, affected by both the exposure to and credit quality of the counterparty, as well as their interactions, all of which are sensitive to market-induced changes. Constructing an effective CCR management framework requires a combination of risk management techniques from the credit, market, and operational risk disciplines. CCR management techniques have evolved rapidly and improved over the last decade even as derivative instruments under management have increased in complexity. While institutions substantially improved their risk management practices, in some cases implementation of sound practices has been uneven across business lines and counterparty types. The financial crisis of 2007–2009 revealed weaknesses in CCR management of timely and accurate exposure aggregation capabilities and inadequate measurement of correlation risks. The crisis also highlighted deficiencies in monitoring and managing counterparty limits and concentrations, ranging from poor selection of CCR metrics to inadequate infrastructure.

The Basel II “Revised Framework” (BCBS 2004) was intended to promote a more forward-looking approach to capital supervision that encourages banks to identify and manage the risks they face. Treatment of CCR arising from over-the-counter (OTC) derivatives and repos in either trading or banking books was first set forth in an amendment to the original 1988 Basel Accord (BCBS 1988) treatments for the CCR of repo-style transactions. The Basel II framework (BCBS 2004) represents joint work with the International Organization of Securities Commissions (IOSCO) on the treatment of CCR for over-the-counter derivatives, repo-style transactions, and securities financing.

The regulations specify three methods for calculating EAD for transactions involving CCR: the internal model method (IMM), a standardized method (SM), and the (at-the-time existing) current exposure method (CEM).

Commonalities across Approaches to CCR

Positions that give rise to CCR exposures share certain generic characteristics. First, the positions generate a credit exposure – the cost of replacing the transaction if the counterparty defaults, assuming there is no recovery of value. Second, exposures depend on one or more underlying market factors. Third, transactions involve an exchange of payments or financial instruments identified with an explicit counterparty having a unique PD.

CCR for a position at any point in time equals a maximum of zero or replacement cost (market value) for each counterparty over tenure. This may include the use of collateral to mitigate risk, legal netting or “rights of offset” contracts, and the use of re-margining agreements. The fact that similar risk characteristics, products, and related activities with CCR are managed by institutions using similar methods and processes imply they may merit similar capital requirements. However, there are differences in rule treatment between OTC exposures and securities financing transactions (SFTs). SFTs include securities lending and borrowing, securities margin lending, and repurchase and reverse repurchase agreements.

The Basel II revised framework (BCBS 2004) already provides three methods for SFTs: a simple approach, a comprehensive approach with both supervisory and nonsupervisory haircuts, and a value-at-risk (VaR) model.

An internal model method (IMM) to CCR is available for both SFTs and OTC derivatives, but the nonmodel methods available for the latter are not applicable to the former. Institutions use several measures to manage their exposure to CCR, including potential future exposure (PFE), expected exposure (EE), and expected positive exposure (EPE). Banks typically compute these using a common stochastic model as shown in Figure 2.1. PFE is the maximum exposure estimated to occur on a future date at a high level of statistical confidence, often used when measuring CCR exposure against credit limits. EE is the probability-weighted average exposure estimated to exist on a future date. EPE is the time-weighted average of individual expected exposures estimated for given forecasting horizons (e.g., one year). EPE is generally viewed as the appropriate EAD measure for CCR as such are treated similarly to loans, and EPE reduces incentives to arbitrage regulatory capital across product types; therefore, internal and standardized model methods employ this for EAD.

Figure 2.1 Expected positive exposure for CCR.

Consistent with the Basel I Revised Framework for credit risk, the EAD for instruments with CCR must be determined conservatively and conditionally on an economic downturn (i.e., a “bad state”; BCBS 1998). In order to accomplish such conditioning in a practical, pragmatic, and conservative manner, the internal and standardized model methods proposed scale EPE using “alpha” and “beta” multipliers. Alpha is set at 1.4 in both the internal model method and the standardized model method, but supervisors have the flexibility to raise alpha in appropriate situations. Banks may internally estimate alpha and adjust it both for correlations of exposures across counterparties and potential lack of granularity across a firm's counterparty exposures. The alpha multiplier is also viewed as a method to offset model error or estimation error. Industry and supervisors' simulations suggest alphas may range from approximately 1.1 for large global dealers to more than 2.5 for new users of derivatives with concentrated or no exposures. Supervisors proposed to require institutions to use a supervisory specified alpha of 1.4 with the ability to estimate a firm portfolio–specific alpha subject to supervisory approval and a floor of 1.2. To estimate alpha, a bank would compute the ratio of economic capital (EC) for counterparty credit risk (from a joint simulation of market and credit risk factors) to EC when counterparty exposures are a constant amount equal to EPE (see Figure 2.2). Under the internal model method, the resulting risk weight may be adjusted to reflect the transaction's maturity.

Figure 2.2 Effective EE and effective EPE for CCR.

Banks may estimate EAD based on one or more bilateral “netting sets,” a group of transactions with a single counterparty subject to a legally enforceable bilateral netting arrangement. Bilateral netting is recognized for purposes of calculating capital requirements within certain product categories: OTC derivatives, repo transactions, and on-balance-sheet loans/deposits. However, under the BCBS Amended Accord and Revised Framework, netting across product categories is not recognized for regulatory capital computation purposes. The intent is to allow supervisors discretion to permit banks to net margin loans secured by purchased securities and executed with a counterparty under a legally enforceable master agreement. This is not intended to permit banks to net across different types of SFTs or to net SFTs against OTC derivatives that might be included in a prime brokerage agreement. The Basel cross-product netting rules recognize such between OTC derivatives and SFTs subject to national supervisor determination that enumerated legal and operational criteria are widely met. A bank should have obtained a high degree of certainty on the legal enforceability of the arrangement under the laws of all relevant jurisdictions in the event of a counterparty's bankruptcy. It is also important that the bank demonstrate to the supervisory authority that it effectively integrates the risk-mitigating effects of cross-product netting into its risk management systems. Requirements are added to those that already exist for the recognition of any master agreements and any collateralized transactions included in a cross-product netting arrangement. Netting other than on a bilateral basis, such as netting across transactions entered by affiliates under a cross-affiliate master netting agreement, is not recognized for regulatory capital computation.

Summary of Regulatory Methods for CCR

The BCBS has articulated the principle that banks should be allowed to use the output of their “own estimates” developed through internal models in an advanced EAD. In order to achieve this, the regulators permit qualifying institutions to employ internal EPE estimates of defined netting sets of CCR exposures in computing the EAD for capital purposes. In general, internal models commonly used for CCR estimate a time profile of EE over each point in the future, which equals the average exposure over possible future values of relevant market risk factors (e.g., interest rates, FX rates). The motivation for this was the need for more consistent treatments and is particularly critical if banks may make use of their own estimates to calculate EAD through an internal model.

Relatively short-dated SFTs pose problems in measuring EPE because estimating a time profile of EE in an internal model only considers current transactions. For some SFT portfolios, the expected exposure might spike up rapidly in the first few days before dropping off sharply at maturity. However, a counterparty may enter new or roll over existing SFTs, generating new exposure not reflected in a current EE time profile. An additional problem arises when short-term are combined with long-term transactions, so that EE is U-shaped, which implies that if short-term transactions roll over, the decline in EE might understate the CCR amount. These issues can also apply to short-term OTC derivatives.

Effective expected positive exposure measurements always lie somewhere between EPE and peak EE. In the case of upward- versus downward-sloping EE profiles, effective EPE will equal EPE or peak EE, respectively. In general, the earlier that EE peaks, the closer effective EPE will be to peak EE; and the later that EE peaks, the closer effective EPE will be to peak EPE. Under the internal model method, a peak exposure measure is more conservative than effective EPE for any counterparty and can be used with prior supervisory approval. While banks generally do not use effective EPE for internal risk management purposes or in economic capital models, it can easily be derived from a counterparty's EE profile.

The consensus is that this is a pragmatic way of addressing rollover of short-dated transactions and differentiating counterparties with more volatile EE time profiles. EEs can be calculated based on risk-neutral or physical-risk factor distributions, the choice of which will affect the value of EE but not necessarily lead to a higher or lower EE. The distinction often made is that the risk-neutral distribution must be used for pricing trades, while the actual distribution must be used for risk measurement and economic capital.

The calculation of effective EPE has elements of both pricing (e.g., in the calculation of an effective maturity parameter) and simulation. Ideally, the calculation would use distribution appropriate to whether pricing or simulation is being done, but it is difficult to justify the added complexity of using two different distributions. Because industry practice does not indicate that one single approach has gained favor, supervisors are not requiring that any particular distribution be used.

Exposure on netting sets with maturity greater than one year is susceptible to changes in economic value from deterioration in the counterparty's creditworthiness short of default. Supervisors believe that an effective maturity parameter (M) can capture the effect of this on capital and the existing maturity adjustment in the revised framework is appropriate for CCR. However, the M formula for netting sets with maturity greater than one year must be different than that employed in the revised framework in order to reflect dynamics of counterparty credit exposures. The approach for CCR provides such a formula based on a weighted average of expected exposures over the life of the transactions relative to their one-year exposures. As in the revised framework, M is capped at five years, and where all transactions have an original maturity less than one year that meet certain requirements, there is CCR-specific treatment.

If the netting set is subject to a margin agreement and the internal model captures the effect of this in estimating EE, the model's EE measure may be used directly to calculate EAD as above. If the internal model does not fully capture the effects of margining, a method is proposed that will provide some benefit, in the form of a smaller EAD, for margined counterparties. Although this “shortcut” method will be permitted, supervisors would expect banks that make extensive use of margining to develop the modeling capacity to measure the impact on EE. To the extent that a bank recognizes collateral in EAD via current exposure, a bank would not be permitted to recognize the benefits in its estimates of LGD.

Supervisory Requirements and Approval for CCR

Qualifying institutions may use internal models to estimate the EAD of their CCR exposures subject to supervisory approval, which requires certain model validations and operational standards. This applies to banks that do not qualify to estimate the EPE associated with OTC derivatives but would like to adopt a more risk-sensitive method than the current exposure method (CEM). The standardized method (SM) is designed both to capture some certain key features of the internal model method for CCR and to provide a simple and workable supervisory algorithm with simplifying assumptions. Risk positions in the SM are derived with reference to short-term changes in valuation parameters (e.g., durations and deltas), and assumed open positions remain over the forecasting horizon. This implies that the risk-reducing effect of margining is not recognized, and there is no recognition of diversification effects.

In the SM, the exposure amount is defined as the product of two factors: (1) the larger of the net current market value or “supervisory EPE” times, and (2) a scaling factor termed beta. The first factor captures two key features of the internal model method (IMM) not mirrored in CEM with respect to netting sets that are deep in the money: The EPE is almost entirely determined by the current market value at the money (current market value is not relevant), and CCR is driven only by potential changes in values of transactions. By summing the current and add-on exposures, CEM assumes that the netting set is simultaneously at and deep in the money. The CEM derives replacement cost implicitly at transaction and not at portfolio level as the sum of the replacement cost of all transactions in the netting set with a positive value. The SM derives current market value for CCR as the larger of the sum of market values (positive or negative) of all transactions in the netting set or zero.