www.quantifisolutions.com

Authored by David Kelly (Quantifi)

• Key data and technology challenges• Current trends and best practices

WHITE PAPERCHALLENGES IN IMPLEMENTING A COUNTERPARTY RISK MANAGEMENT PROCESS

About the Author

David KellyDavid Kelly, Director of Credit Product Development, Quantifi, brings almost 20 years of experience as a trader, quant, and technologist to Quantifi. He has previously held senior positions at some of the largest financial institutions including Citigroup, JPMorgan Chase, AIG and CSFB. At Citigroup, he was the senior credit trader on the Global Portfolio Optimisation desk, responsible for actively managing the credit risk in derivatives positions. Prior to this, he ran the JPMorgan Chase Global Analytics group, where he was responsible for front-office pricing models and risk management tools for the global derivatives trading desks including the firm’s first CVA system. An enrolled member of the Society of Actuaries, Mr.Kelly holds a B.A. in economics and mathematics from Colgate University and has completed graduate work in mathematics at Columbia University and Carnegie Mellon.

Challenges in Implementing a Counterparty Risk Management Process

IntroductionMost banks are in the process of setting up counterparty risk management processes or improving existing ones. Unlike market risk, which can be effectively managed by individual trading desks or traders, counterparty risk is increasingly being priced and managed by a central CVA desk or risk control group since the exposure tends to span multiple asset classes and business lines. Moreover, aggregated counterparty exposure may be significantly impacted by collateral and cross-product netting agreements.

Gathering transaction and market data from potentially many trading systems, along with legal agreements and other reference data, involves significant and often underestimated data management issues. The ability to calculate credit value adjustments (CVA) and exposure metrics on the entire portfolio, incorporating all relevant risk factors, adds substantial analytical and technological challenges. Furthermore, traders and salespeople expect near real-time performance of incremental CVA pricing of new transactions. Internal counterparty risk management must also be integrated with regulatory processes.

In short, the data, technological, and operational challenges involved in implementing a counterparty risk management process can be overwhelming. This paper outlines the key challenges, starting with an overview of the main business objectives, followed by a discussion of data and technology issues, and current trends in best practices.

ObjectivesThe objectives in setting up a counterparty risk management process can be split into three categories – CVA pricing, exposure management, and regulatory requirements. The following is a summary of the objectives to provide context for the data and technology discussions.

CVA PricingCVA is the amount banks charge their counterparties to compensate for the expected loss from default. Since both counterparties can default, the net charge should theoretically be the bilateral CVA, which includes a debt value adjustment (DVA) or gain from the bank’s own default. CVA pricing can be split into the inter-bank and corporate customer markets. New legislation, including the Dodd-Frank Bill in the U.S. and the European Market Infrastructure Regulations (EMIR), along with Basel III, are mandating or incentivising clearing and increased use of collateral over CVA as the principal means for managing counterparty risk in the inter-bank market.

Uncollateralised exposure is more prevalent in the corporate derivatives market and banks compete aggressively on CVA pricing. CVA pricing is inherently complex for two reasons. First, the incremental (or marginal) CVA for each trade should reflect the application of collateral and netting agreements across all transactions with that counterparty. Second, CVA pricing models not only need to incorporate all of the risk factors of the underlying instrument, but also the counterparty’s ‘option’ to default and the correlation between the default probability and the exposure, i.e., right- or wrong-way risk.

Given the complexity, two problems arise. Some banks are not able to compete for lucrative corporate derivatives transactions because they do not take full advantage of collateral and netting agreements with their counterparties in calculating CVA. Or, they win transactions because their models under-price some of the risks and subject the bank to losses. The complexity is compounded by the need for derivatives salespeople to make an executable price in near real-time.

Risk ManagementWhile CVA covers the expected loss from counterparty defaults, there is also the risk of unexpected losses, as well as mark-to-market gains and losses on the CVA. These risks are managed through a combination of exposure limits, reserves and replication or hedging. Unexpected losses are calculated by simulating exposures through time, taking into account netting and collateral agreements, and using the potential future exposure (PFE) profile at a specified percentile, e.g. 99%, and the counterparty’s default probability to calculate the unexpected loss or economic capital (EC), net of CVA.

Many banks strictly rely on reserves and exposure limits to manage these risks but the trend is towards more active management. Hedging CVA has become increasingly important to offset significantly higher regulatory capital requirements and to reduce the impact of CVA volatility on the bank’s earnings. The main problem is that some of the risks can’t be effectively hedged. For example, there may be limited or no liquid CDS referencing some of the counterparties and there are complex correlation and second-order risks that can be difficult to quantify and hedge. The difficulties are amplified by the computational constraints in running Monte Carlo simulations on the entire portfolio for each perturbed input.

Regulatory RequirementsSubject to approval, the Internal Model Method (IMM) specified in the Basel accord allows banks to use their own models to calculate regulatory capital. The counterparty default risk charge is calculated using current market data, either implied or calibrated from historical data. Banks using market-implied or risk-neutral calibrations, appropriate for hedging, must also run the simulations with historical calibrations. Three-years of historical data are required, including a period of stress to counterparty credit spreads. The data must be updated quarterly or more frequently if warranted by market conditions. The counterparty default risk charge is the greater of the charge based on current market data and the charge based on the stress calibration.

Basel III introduces a CVA risk capital charge, as CVA losses were greater than unexpected losses in many cases during the recent crisis. The charge is the sum of the non-stressed and stressed CVA VaR, based on changes in credit spreads over a three-year period. For the stressed CVA VaR, the three-year period includes a one-year period of stress to counterparty credit spreads. Banks need not include securities financing transactions (SFTs) in the CVA risk capital charge unless the risk is deemed material. Cleared transactions are also omitted. However, eligible credit hedges should be included in all calculations. The total regulatory capital charge for counterparty risk is the sum of the counterparty default risk charge and CVA risk capital charge.

Regulatory approval is contingent on model validation. Back-testing of representative portfolios over several historical dates covering a wide range of market conditions is the primary mechanism for validating the capital model. Back-testing involves comparing exposure projections with realised exposures for selected time buckets, e.g., one year. Banks must also perform stress tests on the principal market risk factors to identify general wrong-way risks, concentrations of risks among industries or regions, and large directional, basis and curve risks. Reverse stress testing to identify plausible loss scenarios may also be required.

Summary

The key objectives of the counterparty risk management process can be summarised as follows:

• Central storage of counterparty legal entity structures, including a history of corporate actions and credit events

• Central storage of collateral & netting legal agreements by counterparty

• Ability to load all OTC derivatives and other counterparty transactions from various booking systems

• Collateral position management and integration with counterparty risk calculations

• Market data required to value counterparty transactions, plus market-implied and historical volatilities and correlations for generating exposure distributions, and default probabilities for calculating CVA, DVA and economic capital

• Near-time incremental CVA pricing of new trades, reflecting collateral & netting agreements

• CVA pricing models that incorporate all dimensions of risk, including right- and wrong-way risk

• Expected exposure (EE) and potential future exposure (PFE) profiles for counterparty limit monitoring on a daily basis

• CVA, DVA and economic capital calculations for expected and unexpected loss management

• CVA sensitivities across all risk factors for actively managing counterparty risk

• Credit hedges reflected in exposure and loss metrics, and market hedges reflected in CVA sensitivities

• Regulatory counterparty risk capital calculation using market-implied and historical calibrations, including a stress calibration

• Historical CVA VaR using the recent three-year period and a stress period

• Back-testing of representative portfolios over multiple time periods to validate exposure projection model

• Stress testing to identify wrong-way risks and concentrations

• Reporting of all counterparty risk metrics minimally by counterparty, industry, and region with diagnostic drill-down capabilities

DataBased on the objectives, data requirements can be segregated into reference data, market data, counterparty transactions and collateral positions.

Reference DataReference data generally means detailed terms and conditions for securities and information on legal entities, including historical ratings, corporate actions and credit events. For counterparty risk purposes, the scope expands to netting and collateral agreements, through master agreements and credit support annexes (CSAs). There may be additional netting agreements to facilitate cross-product netting across master agreements.

Collateral agreements impose a cap on counterparty exposure by collateralising the exposure above a specified threshold. For exchange-traded and cleared transactions, the threshold is effectively zero. The threshold(s) may be unilateral or bilateral and may adjust according to the counterparty’s rating or other trigger. Collateral agreements also specify the frequency of collateral calls and the closeout period. The closeout period, or ‘margin period of risk’, is the amount of time between requesting and receiving collateral before default is assumed. The collateral agreement may also specify acceptable types of collateral and corresponding haircuts.

Market DataA fundamental counterparty risk measure is current exposure (CE), or the mark-to-market value of all positions by counterparty, which may be netted and collateralised according to legal agreements stored in the reference database described above. In order to calculate CE, all market data required by curve calibrators and pricing models must be sourced. For a large trading book, this can mean quoted prices and volatilities across interest rates, FX, commodities, equities and credit.

The market-implied volatilities used by pricing models to mark positions can also be inputs to the simulation engine that generates market scenarios and the distribution of counterparty exposures through time. Risk metrics such as EE and PFE are calculated from these exposure distributions. CVA, DVA and economic capital also require counterparty default probabilities, derived from credit spreads or historical data. Correlations between the counterparty’s default probability and each risk factor are needed to value right- and wrong-way risk. Correlations between risk factors may also be needed to manage the dimensionality of the simulations.

The Basel guidelines impose further requirements on market data. While current market-implied data may be used for some calculations, Basel III forces banks to use historical volatilities and correlations, calibrated over a three-year period, in generating exposure distributions. An additional three-year period that includes a period of significant stress to counterparty credit spreads is required for the default risk capital and CVA VaR calculations. The historical data set should also support back-testing of the exposure model. Banks are expected to ensure that market data is scrubbed and verified, stored in a secure database, and independent of business lines.

TransactionsIn most cases, OTC derivatives account for the largest share of counterparty risk. Securities financing transactions typically make up another significant portion of transactions but are often less risky due to shorter maturities. Hedges such as CDS and other credit derivatives should also be included. While only specific credit hedges are recognised for regulatory capital relief, other products referencing underlying market risk factors may be used to hedge CVA sensitivities.

OTC transactions pose the biggest challenge. For most banks, the vast majority are interest rate and FX derivatives. OTC derivatives may contain customised cash flows and/or exotic payoffs, which must be communicated to the counterparty risk system. Some of the economic terms and conditions may also come from the reference database.

Counterparty collateral positions are also important. Most collateral is cash and highly rated securities, although the increased emphasis on collateral funding is causing banks to explore other forms. Ideally, the counterparty risk system would revalue the collateral along with the transactions in calculating net counterparty exposure in order to reflect mark-to-market risk of the collateral.

ReferenceData

Transactions Collateral

Market Data

• Securities • Legal entities • Ratings • Corporate actions • Credit events • Master agreements

& CSAs • Cross-product

netting agreements

• OTC derivatives • Securities Financing

Transactions • Credit hedges • CVA market hedges

• Cash• Securities• New types• Haircuts• Funding

• IR,FX,commodities & equities prices

• Creditspreads(PDs)• Marketvolatilities• Historicalvoltilities• Correlations• Historicaldata

TechnologyThe counterparty risk management objectives and corresponding data requirements summarised in the previous two sections translate into a very challenging technology agenda. Data management is certainly a top priority, as well as robust CVA pricing and risk analytics. Centralising counterparty risk management for the entire trading book places a high priority on scalability, while providing near-time performance for marginal pricing of new trades. Given the large amount of information, a configurable reporting environment is a necessity. For regulatory approval, the infrastructure must also support back-testing, stress testing and VaR. Each of these issues is addressed below.

Data ManagementMost banks have multiple systems for reference data, market data, and transactions, which may be different for each business unit. These systems may be further sub-divided into front-office analytical tools and back-office booking systems, each with its own repository of reference data. There may also be several internal and external sources for market data. The counterparty risk system must integrate with potentially many of these systems in order to extract the data needed to produce a comprehensive set of counterparty risk metrics.

Systems integration presents several technical challenges. Source systems may be built with a variety of technologies, such as C++, Java, and .NET, which means the counterparty risk system may have to ‘speak’ many languages. Some systems may have well-developed interfaces and APIs while others may not, which not only adds to the cost of development but also increases the failure rate in terms of missing or erroneous data.

Related issues include symbology mapping and data conversion. Source systems may use their own symbols to identify securities and other parameters, such as holiday codes and reference indices. Use of industry standard ticker symbols lessens the problem but invariably, the counterparty risk system must be able to interpret a variety of naming conventions. Source systems also store data in proprietary formats. While loading the data, the counterparty risk system must convert it into its own format. Expanded use of open XML formats, such as FIXML and FpML, as more products become standardised for clearing, is a positive development.

Once the systems have been connected and data translations in place, the counterparty risk system must receive incremental updates from the upstream systems. Daily updates are sufficient for most purposes but incremental CVA pricing depends on up to date position and market data.

AnalyticsAnalytical components can be categorised into pricing models, the simulation engine, and market data calibrators. Most institutions have their own proprietary libraries of pricing models for front-office pricing and hedging. Ideally, the bank would use the same pricing models for counterparty risk to ensure consistency. Market data calibrators provide the market-implied and historical volatilities and correlations as inputs to the simulation engine.

The simulation engine uses pricing models in generating the exposure distribution over a specified set of future dates. The first step is to simulate market scenarios using a risk neutral or historical calibration. The next step is to value each transaction over all scenarios and time steps to create the exposure distribution. The exposures are aggregated by counterparty ‘netting set’, according to legal netting agreements. Collateral terms are then applied to determine uncollateralised exposure by netting set.

In applying collateral thresholds, the ‘margin period of risk’ and collateral mark-to-market risk must be considered. EE, PFE and various Basel metrics including expected positive exposure (EPE) can be directly calculated from the exposure distribution. The final step is to ‘integrate’ the exposure distribution to calculate CVA, DVA and economic capital. Incorporating right- and wrong-way risk into these calculations is important, especially for credit derivative transactions, and non-trivial to implement.

Several analytical issues arise in performing the above steps. First and foremost, pricing models must be able to perform forward valuations for the simulated market scenarios. Early termination or mutual ‘put’ features should be reflected. For path-dependent and other exotic payoffs, the simulation engine should provide sufficient path-level information to the pricing model to prevent valuation inconsistencies (and performance bottlenecks) from having to run simulations within the simulation.

In order for pricing models to re-use market scenarios or paths, the simulation engine should use risk neutral calibrations of the various risk factors. For example, if pricing models use a Libor market model, e.g., BGM, to price interest rate derivatives, the simulation engine should use the same model, which means calibrating forward rates, volatities and correlations from current market data. This calibration can also be used to calculate CVA sensitivities for hedging purposes. The simulation engine must also support calibrations based on historical data to calculate the various metrics required by Basel.

Performance & ScalabilityFor a large bank, the counterparty risk system may price something on the order of one million transactions over one thousand scenarios and one hundred time steps, or 100 billion valuations. If the bank actively hedges CVA, the number of valuations is roughly multiplied that by the number of sensitivities required. Pricing models must be as efficient as possible in order to generate counterparty risk metrics on a daily basis. Since banks prefer to use their own pricing models for consistency, additional tuning or substitution of less sophisticated models for valuation of complex products, at the expense of some accuracy, may be necessary. Another approach is to use pre-calculated pricing grids to reduce the number of valuations.

Scalability can also be addressed through parallel processing. Distributing computations across servers and processor cores using grid technology and multi-threading should allow acceptable levels of performance to be achieved by adding hardware resources. Of course, complexity and potential for failure increases with the amount of hardware.

Re-running simulations on the entire portfolio to determine the impact (marginal price) of a new transaction is not practical for intra-day pricing. The typical solution involves saving valuations at the netting set level for each scenario and time step in the database. Marginal pricing is then reduced to simulating the new trade along the same scenarios and time steps and aggregating with the saved netting set valuations, re-applying netting and collateral agreements. Of course, adjustments or re-simulation may be necessary for changes in portfolio composition or significant market moves.

ReportingWith the huge amount of data involved and analytical complexity, the ability to view the various counterparty risk metrics across a variety of dimensions is absolutely essential. At the very least, the system should show CE, EE, PFE, CVA, DVA and economic capital by counterparty, industry and region. The system should also display EE and PFE profiles along specified future time buckets out to the maximum maturity date. The ability to inspect reference, market and transaction data inputs is vital in verifying calculated results and tracking down errors. The system must also provide reports for back-testing, stress testing and VaR outputs with similar aggregation and drill-down capabilities.

Back-testing,StressTesting&VaRThe counterparty risk system’s infrastructure must provide back-testing, stress testing and full-reval historical VaR. For back-testing, the system should record all data necessary to simulate exposures for representative portfolios over multiple historical dates. Stess testing functionality should support configuration of a set of shifts to any or all of the current market data used to value the portfolio. Flexibility to ‘steepen’ or ‘flatten’ curves must be provided, as well as shifting basis spreads. VaR is similar to stress testing in that it involves a set of shifts. The shifts are derived from a time series of historical market data, typically daily. In some institutions, the market risk group provides a set of shift ‘files’ that the risk system should load to run VaR and stress tests.

•Simulation engine •Risk-neutral & historical

calibrations • Netting & collateral • Right/wrong-way risk • Exotic payoffs

•Large cross-asset portfolios

•Pricing model performance

•Near-time CVA pricing • CVA sensitivities • Daily and intra-day

metrics

•CE, EE, PFE, CVA, EC by counterparty, industry & region

•EE & PFE profiles over time

•Data inspection & drill-downs

•Back-testing, stress testing, VaR outputs

•Back-test exposure projections vs. Realizations

•Curve steepening & flattening stress scenarios

•Stress basis spreads •Full reval historical VaR

•Multiple data sources •Systems integration •Symbology mapping •Data format

conversions •Incremental updates

Back-testing, StressTesting & VAR

Performance & ScalabilityData Management

Analytics Reporting

TrendsPost crisis, the ability for senior management to get a comprehensive view of the bank’s counterparty risks is a critical priority. Consolidated risk reporting has been elusive due to front-office driven business models. As influential revenue producers, trading desks have maintained a tight grip on data ownership, model development and front-office technology. This has resulted in a proliferation of systems, making the job of aggregating risks across business lines excessively complicated. Continuous development of new types of derivative payoffs and structured products has exacerbated the problem. But the failures and near failures of several global banks have changed the traditional mentality. Banks are now taking a ‘top-down’ approach to risk management. Decision-making authority is transitioning from the front-office to central market and credit risk management groups. This authority includes tighter controls on data and technology.

A key component of the top-down approach to risk management is the central CVA desk or counterparty risk group. This group is responsible for marginal CVA pricing of new trades originated by the individual business units and then managing the resultant credit risk. In practice, the CVA desk sells credit protection to the originating trading desk, insuring them against losses in the event of a counterparty default. There are several advantages to this approach. Housing counterparty risk in one place allows senior management to get a consolidated picture of the exposures and proactively address risk concentrations and other issues. It also addresses the competitive CVA pricing issues described in a previous section. As banks continue to ramp up active management of CVA, having a specialised group allows careful management of complex risks arising from liquidity, correlation and analytical limitations.

The reasons for not creating a central CVA desk or counterparty risk group tend to be practical issues particular to the institution. Decentralised infrastructures may make the data and technology challenges too great to ensure provision of meaningful consolidated counterparty risk metrics on a timely basis. Some banks have aligned counterparty risk management by business line in order to more effectively manage the data and analytical issues at the expense of certain benefits, like netting. For centralised CVA desks, there is also the challenge of internal pricing and P&L policies. Most banks position CVA desks as utility functions that simply attempt to recover hedging costs in CVA pricing.

Recent regulatory activity has also had a profound impact on counterparty risk management, mostly due to central clearing requirements and higher capital ratios. Mandating central clearing for an expanding scope of derivative products effectively moves counterparty risk out of complex CVA and economic capital models and into more deterministic and transparent margining formulas. The heavily collateralised inter-dealer market is also undergoing significant changes due to the widening of Libor basis spreads during the crisis. A new standard for pricing collateralised trades is emerging, based on OIS discounting. Institutions are now looking more closely at optimising collateral funding through cheapest-to-deliver collateral, re-couponing existing trades to release collateral, and moving positions to central counterparties in order to access valuation discrepancies or more favorable collateral terms.

It is expected that most corporate derivatives transactions will remain exempt from clearing mandates since banks provide valuable hedging services in the form of derivative lines. The cost of extending these lines is increasing due to significantly higher regulatory capital requirements. Therefore, competitive CVA pricing and economic capital optimisation will remain priorities for corporate counterparty risk management alongside collateral and clearing processes.

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