Quantitative Financial Risk Management. Galariotis Emilios
returns and increases in CDS spreads for creditors, and that creditors with large exposures are more likely to suffer from financial distress later, suggesting that counterparty risk is a potential additional channel of credit contagion. Arora, Gandhi, and Longstaff (2012) use proprietary data from 14 CDS dealers and find that counterparty risk is priced in the CDS market and the magnitude of the effect is small. Brigo, Capponi, Pallavicini, and Papatheodorou (2013) value bilateral CCR through stochastic dynamical models when collateral is included with possible rehypothecation. The authors show for credit default swaps that a perfect collateralization cannot be achieved under default correlation.
Brigo, Buescu, and Morini (2012) compare two different bilateral counterparty valuation adjustment formulas (an approximation based on subtracting the two unilateral credit valuation adjustment formulas as seen from the two different parties in the transaction) and a fully specified bilateral risk formula where the first-to-default time is taken into account. Finally, Acharya and Bisin (2014) study financial markets where agents share risks but have incentives to default and their financial positions might not be transparent, that is, not mutually observable. The authors show that a lack of position transparency results in a counterparty risk externality, which manifests itself in the form of excess “leverage” in that parties take on short positions that lead to levels of default risk that are higher than Pareto-efficient ones.
Supervisory Requirements for CCR
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