Quantitative Financial Risk Management. Galariotis Emilios

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.