Credit and Counterparty Credit Risk

Introduction and context

Credit risk is the possibility that a borrower, issuer, or trading counterparty fails to meet its contractual obligations, causing a financial loss. In investment banking, this risk extends beyond traditional lending. It arises in corporate loans, bonds, underwriting commitments, securities financing transactions, derivatives, prime brokerage, settlement, clearing, and structured products.

Counterparty credit risk (CCR) is the risk that the counterparty to a transaction defaults before the final settlement of the transaction, while the bank has a positive economic exposure to that counterparty. Unlike the credit risk of a funded loan, CCR is often bilateral, changes over time, and depends on market variables. A derivatives counterparty may be out-of-the-money today but become substantially in-the-money tomorrow.

Effective credit-risk management therefore combines obligor analysis, quantitative measurement, legal documentation, collateral and margining, portfolio diversification, capital allocation, stress testing, and governance. The central analytical chain is:

where PD is probability of default, LGD is loss given default, and EAD is exposure at default. This relationship is a useful foundation, but it does not capture every dimension of risk. A bank must also assess unexpected loss, concentration, migration, wrong-way risk, liquidity effects, model uncertainty, and the cost of hedging or closing positions.

Learning objectives

By the end of this chapter, the reader should be able to:

• distinguish credit risk from counterparty credit risk;
• evaluate obligors using financial analysis, ratings, and internal credit assessment;
• calculate and interpret PD, LGD, EAD, expected loss, and unexpected loss;
• explain credit migration and its effect on portfolios and capital;
• analyse netting, collateral, margin, and legal enforceability;
• identify general and specific wrong-way risk;
• explain the purpose and structure of SA-CCR;
• describe credit valuation adjustment (CVA), its drivers, and its limitations;
• apply these concepts to investment-banking decisions and governance.

1. The architecture of credit risk

1.1 Obligor risk and counterparty risk

An obligor is an entity that owes money under a financial contract. Obligor risk generally concerns the creditworthiness of that entity. Counterparty risk is transaction-specific: the amount at risk depends on the current replacement cost and future market value of the contract.

For a fixed-rate corporate loan, exposure is usually close to the outstanding principal, subject to undrawn commitments and recoveries. For an interest-rate swap, exposure may be zero, positive, or negative and can change daily. A bank that is owed money has positive exposure; a bank that owes money has no current credit exposure on that transaction, although future exposure may arise.

1.2 The default event

A default event may include failure to pay, bankruptcy, restructuring that disadvantages creditors, breach of financial covenants, or another event defined in the legal documentation. The definition matters because PD statistics, recovery assumptions, close-out rights, and accounting outcomes depend on the default standard used.

Default risk should be separated from downgrade or migration risk. A bond can lose market value because its rating deteriorates even when the issuer continues to pay. Thus, a portfolio may incur mark-to-market losses before any default occurs.

1.3 Credit-risk lifecycle

Credit risk is managed throughout the transaction lifecycle:

1. Origination: identify the borrower or counterparty, purpose, structure, tenor, and risks.
2. Underwriting: assess repayment capacity, collateral, covenants, and pricing.
3. Approval: apply delegated authority, limits, and independent challenge.
4. Execution: ensure documentation, confirmations, netting agreements, and collateral terms are valid.
5. Monitoring: track financial results, ratings, exposures, collateral, market indicators, and covenant compliance.
6. Remediation: use waivers, restructuring, hedging, collateral calls, or reductions in exposure.
7. Default and recovery: enforce rights, close out positions, realise collateral, and measure the final loss.

2. Assessing an obligor and counterparty

2.1 Qualitative assessment

A professional credit assessment begins with the business and its economic environment. Important questions include:

• How stable are revenues, margins, and cash flows?
• Is the business cyclical, regulated, technologically vulnerable, or exposed to commodities or foreign exchange?
• What is management’s record in capital allocation and financial reporting?
• How concentrated are customers, suppliers, geography, and funding sources?
• What is the entity’s access to liquidity during stress?
• Are legal entities, guarantees, and ownership structures transparent?

For a financial institution, analysis includes asset quality, capital, liquidity, funding stability, market-risk sensitivity, resolution regime, and the quality of risk governance.

2.2 Quantitative financial analysis

Core measures include leverage, interest coverage, liquidity, profitability, and cash-flow conversion. For example:

These ratios are meaningful only when definitions are consistent. EBITDA may exclude unusual items, and total debt may or may not include leases, securitisations, pensions, or guarantees. Trend analysis, peer comparison, stress cases, and covenant headroom are generally more informative than a single ratio.

Cash-flow analysis should distinguish accounting earnings from cash available for debt service. A borrower with strong reported earnings but weak operating cash flow may be vulnerable to refinancing or working-capital shocks.

2.3 External ratings and internal ratings

External credit ratings provide a common language for relative credit quality. They support investment mandates, pricing, limit frameworks, and regulatory classification, but they are not substitutes for independent analysis. Ratings may be delayed, may differ among agencies, and may not reflect transaction-specific collateral, seniority, maturity, or wrong-way risk.

Banks commonly use internal rating systems. An internal grade may be based on financial ratios, industry risk, qualitative judgement, behavioural data, market indicators, and expert overrides. A robust rating system should have:

• a clearly defined scale and default point;
• documented rating criteria;
• independent validation;
• stable application across comparable obligors;
• controls over overrides and conflicts of interest;
• evidence that grades discriminate risk and are appropriately calibrated.

Rating models should not be treated as mechanical truth. Model drift, sparse defaults, changing macroeconomic conditions, and management judgement create uncertainty.

3. PD, LGD, EAD and expected loss

3.1 Probability of default

PD is the probability that an obligor defaults over a specified horizon, commonly one year or the remaining life of an instrument. A one-year PD and lifetime PD are not interchangeable. PD may be estimated using historical default frequencies, statistical models, market-implied spreads, structural models, or a combination of methods.

A conditional, or hazard, default probability for period t can be represented as:

If period hazards are known and defaults are mutually exclusive, the cumulative probability of default by time T is:

This formula assumes the hazards are defined conditionally on survival and are measured consistently. It should not be confused with simply adding annual PDs, which can exceed 100% and ignores survival.

3.2 Loss given default

LGD is the proportion of exposure not recovered after default, including direct recovery, collateral proceeds, guarantees, workout costs, legal costs, and the time value of money. A simplified expression is:

If recovery cash flows occur after default, a discounted recovery rate is more appropriate:

LGD depends on seniority, collateral, jurisdiction, industry, economic conditions, cure rates, and the speed of the workout. Recovery estimates should be consistent with the exposure definition and discount-rate convention.

3.3 Exposure at default

EAD is the amount exposed to loss when default occurs. For a funded loan it may approximate drawn principal plus accrued interest and certain fees. For a revolving facility, future drawings must be considered. For derivatives, EAD is uncertain and depends on replacement cost, potential future exposure, netting, collateral, and market volatility.

For a commitment, a simplified estimate is:

where CCF is the credit conversion factor. This is an estimate, not an accounting identity; its suitability depends on product type, borrower behaviour, contractual cancellation rights, and the observation horizon.

3.4 Expected loss

Expected loss is the probability-weighted average loss over a specified horizon:

For multiple exposures, portfolio expected loss is the sum of individual expected losses when each loss is measured consistently:

This aggregation does not mean that portfolio losses are certain or independent. It is an average outcome over many comparable risk states. Pricing should also consider funding, capital, liquidity, operating costs, hedging, and return requirements.

Worked example 1: loan expected loss

A bank has a £20 million senior unsecured loan. The one-year PD is 2.5%, and estimated LGD is 45%.

The one-year expected loss is £225,000, or 1.125% of EAD. This does not imply that the bank will lose exactly £225,000 next year. It is the long-run average loss estimate under the stated assumptions.

4. Unexpected loss, concentration and economic capital

Actual losses vary around expected loss. Unexpected loss (UL) represents the adverse variability of losses and is commonly measured using a high percentile of a loss distribution, less expected loss:

The confidence level and time horizon must be specified. Economic capital is the capital judged necessary to absorb unexpected losses at a chosen solvency standard. It is not identical to regulatory capital, although regulatory frameworks may use related risk concepts.

Portfolio UL depends heavily on dependence among obligors. A diversified portfolio can have lower relative volatility than a concentrated portfolio with the same total EL. Correlations often increase during crises, making diversification benefits smaller precisely when they are most needed.

Concentration may be:

• Name concentration: excessive exposure to one borrower or group;
• Sector concentration: correlated borrowers in one industry;
• Geographic concentration: common sovereign, legal, or macroeconomic exposure;
• Product concentration: similar loans, derivatives, or collateral;
• Risk-factor concentration: exposure to the same interest rate, commodity, currency, or funding shock.

Limits, portfolio reporting, stress testing, syndication, credit insurance, and hedging are tools for controlling concentration.

5. Credit migration and portfolio dynamics

Credit migration is movement between credit-quality states, such as rating grades, over time. A migration matrix records transition probabilities. For grades A, B, and Default, a one-year matrix might conceptually be:

Each row should sum to 100%. The matrix may be estimated from historical observations and conditioned on economic conditions. A downgrade can widen credit spreads and reduce market value even without default. Therefore, trading-book credit risk includes migration risk as well as default risk.

For repeated periods, a transition matrix P can be used to estimate multi-period transitions:

Here T is the number of equal periods and the matrix multiplication assumes a time-homogeneous Markov approximation: the next state depends on the current state rather than the full history. This assumption is convenient but imperfect; credit quality often exhibits duration dependence and macroeconomic dependence.

Migration analysis supports stress testing, bond portfolio management, loan pricing, limit setting, and capital planning. It is especially important for leveraged finance, where a downgrade can trigger collateral calls, covenant breaches, forced sales, or refinancing difficulty.

6. Counterparty credit exposure

6.1 Current exposure and potential future exposure

For a derivative, current exposure is generally the positive replacement cost after legally enforceable netting and collateral. If the mark-to-market value to the bank is negative, current exposure is normally zero before considering other adjustments:

Potential future exposure (PFE) estimates how exposure may increase over a specified horizon at a high percentile. Expected positive exposure (EPE) is the time-weighted average of expected positive exposure across future dates. These measures differ from current exposure because market variables can change.

A simplified unsecured exposure profile can be written as:

where V_t is the net portfolio value at time t and C_t is eligible collateral, subject to contractual haircuts, thresholds, minimum transfer amounts, margin frequency, and legal enforceability.

6.2 Netting

Close-out netting allows a bank to aggregate positive and negative replacement values with the same counterparty under an enforceable master agreement. Without netting, a bank might owe money on one swap while being owed money on another, yet be unable to offset the positions after default.

Netting is not merely an operational convention. It is a legal conclusion dependent on jurisdiction, documentation, insolvency law, product scope, and the relationship between entities. Netting sets must therefore be identified at legal-entity level, and legal opinions must be current and sufficiently comprehensive.

6.3 Collateral and margin

Collateral reduces exposure if it is eligible, liquid, correctly valued, segregated where necessary, and enforceable. Common collateral includes cash and government securities. A haircut protects the secured party against price movements and liquidation costs:

For example, £10 million of securities with a 5% haircut contributes £9.5 million of risk-reducing value. Collateral creates its own risks: disputes, settlement delays, wrong-way collateral, concentration, liquidity, rehypothecation, and legal failure.

Variation margin covers current mark-to-market exposure; initial margin is intended to cover potential changes during the margin period of risk. Central clearing can reduce bilateral exposures through multilateral netting, but it concentrates risk in central counterparties and introduces default-fund, liquidity, margin, and interoperability risks.

7. Wrong-way risk

Wrong-way risk occurs when exposure to a counterparty increases at the same time that the counterparty’s credit quality deteriorates.

General wrong-way risk arises from broad dependence between exposure and credit quality. For example, a bank may have large exposures to firms whose revenues depend on the same recession-sensitive economy.

Specific wrong-way risk arises from a direct relationship between the counterparty and the exposure. Examples include a derivative whose value rises when the counterparty’s own share price collapses, or a guarantee secured by assets whose value is tied to the guarantor’s solvency.

Wrong-way risk invalidates simplistic assumptions that PD, LGD, and exposure are independent. It requires transaction screening, stress testing, exposure simulation under joint market-credit scenarios, limits, enhanced collateral, and sometimes rejection of the transaction. Correlation estimates alone are insufficient because dependence can become nonlinear in stress.

8. SA-CCR: a regulatory exposure framework

The Standardised Approach for Counterparty Credit Risk (SA-CCR) is a regulatory method for calculating exposure at default for derivatives. Its broad structure is:

where α is a regulatory multiplier, RC is replacement cost, and PFE is potential future exposure. Under the Basel framework, α is generally 1.4, subject to the applicable jurisdiction and regulatory rules.

Replacement cost reflects current exposure after eligible collateral and contractual terms. PFE is built from supervisory add-ons for asset classes such as interest rates, foreign exchange, credit, and commodities. The method recognises netting sets, maturity, collateral, supervisory factors, and hedging sets.

At a high level, SA-CCR proceeds as follows:

1. identify the legally enforceable netting set;
2. calculate the net replacement cost and collateral effect;
3. classify trades into asset classes and hedging sets;
4. determine supervisory-adjusted notionals or sensitivities;
5. calculate asset-class add-ons and aggregation effects;
6. apply the multiplier and α;
7. use EAD in the relevant capital calculation.

SA-CCR is deliberately standardised. It is not a full simulation of the bank’s actual exposure distribution. It may be conservative for some portfolios and insufficiently sensitive to certain basis, liquidity, concentration, or wrong-way risks. Banks should therefore supplement it with internal exposure simulation, stress testing, limit monitoring, and management overlays.

9. Credit valuation adjustment

CVA is the adjustment to the risk-free value of a derivative or derivative portfolio for the possibility that the counterparty defaults before the transaction is completed. For a simplified unilateral setting, CVA can be expressed conceptually as:

where DF_t is the discount factor to time t, EE_t is expected positive exposure, PD_increment_t is the probability of default during period t, and LGD_t is loss given default. This expression assumes exposure and default probability are treated in a simplified manner and does not fully capture dependence between exposure and default.

A more general continuous representation is:

The integral sums discounted expected positive exposure weighted by incremental default probability. The sign and presentation of CVA depend on valuation convention; in a typical dealer valuation, CVA reduces the value of a receivable from the counterparty.

CVA is sensitive to exposure profiles, volatility, maturity, netting, collateral, counterparty spreads, recovery assumptions, discounting, and wrong-way risk. A bank’s CVA desk may hedge spread risk, interest-rate risk, foreign-exchange risk, and exposure-profile risk. Hedges themselves introduce basis, liquidity, model, and counterparty risk.

Worked example 2: simplified CVA

Assume a one-year transaction has expected positive exposure of €5 million, one-year PD of 3%, LGD of 60%, and a discount factor of 0.98. Ignoring timing within the year and assuming independence:

This is an illustrative unilateral estimate, not a complete regulatory or accounting CVA calculation. In practice, exposure varies by date, default probabilities are term-structured, collateral changes exposure, and wrong-way dependence may materially alter the result.

10. Integrated illustrative case: an investment-bank derivatives client

Background

This fictional case concerns Meridian Bank, which is considering a three-year interest-rate and foreign-exchange derivatives package for Orion Energy, a highly leveraged energy company. The proposed trades have a notional amount of €400 million, but notional is not itself the credit exposure. The bank and Orion intend to use a master netting agreement with daily variation margin.

Problem

Orion’s credit rating is one grade below investment grade. Its PD is expected to rise sharply if energy prices fall. The derivatives become more valuable to Meridian when energy prices decline because of the structure of Orion’s hedges. Orion’s credit quality and Meridian’s exposure may therefore deteriorate together. The initial margin terms are modest, and Orion proposes posting shares of an energy affiliate as collateral.

Analysis

The credit team identifies several risks:

• the rating does not fully capture the potential downgrade under an energy-price shock;
• current exposure is low, but three-year PFE is material;
• the proposed equity collateral is volatile and highly correlated with Orion’s solvency;
• the netting agreement is enforceable only if the relevant Orion legal entities are correctly included;
• daily margin reduces exposure but does not eliminate gap risk, valuation disputes, or liquidation risk;
• specific wrong-way risk exists because the collateral and counterparty credit quality are linked to the same energy sector.

A simplified base-case estimate uses PD of 4%, LGD of 55%, and EAD of €30 million:

A stress case assumes PD of 12%, LGD of 65%, and EAD of €55 million:

The stress loss is more than six times the base-case estimate. This result reflects simultaneous deterioration in default probability, recovery, and exposure, which is precisely why single-factor expected loss can be misleading.

Approach and solution

Meridian should require:

1. a refreshed independent credit assessment and energy-price stress analysis;
2. confirmation that the master agreement and netting opinion cover all relevant entities and products;
3. eligible liquid collateral with conservative haircuts rather than concentrated affiliate equity;
4. a higher initial-margin requirement or shorter margin period of risk;
5. counterparty and wrong-way-risk limits approved by an independent risk committee;
6. SA-CCR exposure measurement supplemented by simulation and stress testing;
7. CVA pricing that includes the expected exposure profile and Orion’s credit spread;
8. monitoring triggers for rating migration, collateral concentration, margin disputes, and liquidity deterioration;
9. contingency plans for close-out, hedge replacement, and collateral liquidation.

Outcome

The transaction is approved only after the collateral package is redesigned, the limit is reduced, and pricing is increased to compensate for expected loss, CVA, capital, funding, and hedging costs. The bank also prohibits additional correlated energy exposures to Orion without senior approval.

Learning points

The case demonstrates that notional amount is not exposure, legal enforceability is essential to netting, collateral may create rather than reduce wrong-way risk, and stress testing must vary PD, LGD, and EAD together. It also illustrates the distinction between a regulatory exposure measure and a broader risk-management view.

11. Governance, regulation and ethical considerations

Credit risk is a governance discipline as much as a quantitative exercise. The three-lines model is useful: front-office and business units own risks and controls; risk management provides independent oversight; internal audit provides assurance.

Important controls include segregation of duties, credit approval authorities, limit monitoring, independent valuation, model validation, collateral reconciliation, legal-opinion management, data lineage, and escalation procedures. Exceptions should be documented, time-limited, and visible to senior management.

Regulatory requirements differ by jurisdiction and evolve over time. Institutions must consult applicable Basel-based capital rules, accounting standards, margin requirements, central-clearing rules, large-exposure limits, and local insolvency law. A textbook formula should never replace the current rulebook or legal advice.

Ethically, analysts should avoid rating shopping, optimistic recovery assumptions, hidden concentrations, and selective stress scenarios. Models should be explainable enough for decision-makers to understand material drivers. AI and machine learning may improve early-warning detection or document analysis, but they introduce risks involving data quality, bias, explainability, drift, privacy, and overreliance. Human accountability remains essential, especially for overrides, client selection, and limit exceptions.

12. Best practices and limitations

A mature framework should:

• combine quantitative models with expert judgement;
• distinguish default, migration, spread, liquidity, and settlement risks;
• use consistent definitions of PD, LGD, EAD, default, and recovery;
• validate models using discrimination, calibration, stability, and back-testing;
• stress correlations, collateral values, margin periods, and recovery timing;
• recognise legal-entity and jurisdictional differences;
• monitor wrong-way risk explicitly;
• reconcile front-office, risk, finance, collateral, and regulatory data;
• price expected loss, capital, funding, CVA, and operational costs;
• maintain credible default-management and recovery procedures.

No model eliminates uncertainty. Historical data may contain few severe defaults, market-implied PDs may include liquidity and risk premia, and rating transitions may change during structural economic shifts. Regulatory standardisation promotes comparability but cannot replace institution-specific judgement. The strongest practice is therefore layered: sound underwriting, enforceable documentation, diversified limits, robust measurement, scenario analysis, and decisive governance.

Chapter Summary

Credit and counterparty credit risk concern the possibility that an obligor or trading counterparty fails to perform. The fundamental expected-loss model is EL = PD × LGD × EAD, but effective risk management also considers unexpected loss, concentration, migration, market-value changes, liquidity, and model uncertainty.

PD measures default likelihood over a defined horizon; LGD measures the proportion lost after recovery; and EAD measures the exposure when default occurs. Credit migration can produce losses before default through spread widening and rating deterioration. Counterparty exposure varies through time and is affected by netting, collateral, margin, market volatility, and legal enforceability.

Wrong-way risk is especially important when exposure and credit deterioration are positively related. SA-CCR provides a standardised regulatory EAD framework using replacement cost and potential future exposure. CVA adjusts derivative value for counterparty default risk and depends on expected exposure, default probabilities, recovery, discounting, and dependence. Professional practice requires independent assessment, validated models, enforceable documentation, stress testing, ethical governance, and continuous monitoring.

Key Terms

PD: Probability that an obligor defaults over a specified horizon.

LGD: Loss proportion after recoveries, costs, and relevant discounting.

EAD: Exposure expected to be outstanding when default occurs.

Expected loss: Probability-weighted average credit loss.

Unexpected loss: Loss variability above expected loss at a selected confidence level.

Obligor: Entity that owes money under a financial contract.

Counterparty credit risk: Risk of loss from a trading counterparty defaulting before settlement.

Credit migration: Movement between credit-quality or rating states.

Rating: External or internal assessment of relative credit quality.

Netting: Contractual offsetting of payable and receivable amounts.

Collateral: Assets pledged to reduce credit exposure.

Haircut: Reduction applied to collateral market value for risk protection.

Wrong-way risk: Exposure increasing as counterparty credit quality deteriorates.

Replacement cost: Current positive value of a transaction or netting set.

Potential future exposure: Estimate of possible future positive exposure.

SA-CCR: Standardised Approach for Counterparty Credit Risk.

CVA: Valuation adjustment for counterparty default risk.

Recovery rate: Proportion of EAD recovered after default.

Economic capital: Capital held against unexpected loss at a chosen risk standard.

Netting set: Transactions covered by an enforceable netting agreement.

Review Questions

Short Answer Questions

1. Distinguish traditional obligor credit risk from counterparty credit risk.
2. Define PD, LGD, and EAD.
3. Why can a credit downgrade cause a loss before default?
4. What is the purpose of collateral haircuts?
5. Explain the difference between general and specific wrong-way risk.

Long Answer Questions

1. Explain how an investment bank should assess a corporate derivatives counterparty from origination through default management.
2. Discuss the assumptions, uses, and limitations of EL = PD × LGD × EAD.
3. Explain how netting, collateral, margin, and legal enforceability affect counterparty exposure.
4. Describe SA-CCR and compare its regulatory purpose with internal exposure simulation.
5. Explain CVA, its principal drivers, and the risks involved in CVA hedging.

Analytical / Application Questions

1. A £50 million facility has a one-year PD of 3%, LGD of 40%, and EAD equal to the facility amount. Calculate expected loss and interpret the result.
2. A derivatives portfolio has current positive exposure of €4 million, eligible collateral of €3 million after haircuts, and a stressed potential future exposure of €6 million. Explain why current exposure and future exposure should be assessed separately, and identify additional information needed for a full EAD estimate.
3. A counterparty posts shares issued by its own parent company as collateral. Analyse the potential wrong-way, liquidity, valuation, and legal risks and recommend risk mitigants.

Multiple Choice Questions with Answers

Question 1

Which expression represents the basic expected-loss relationship?

A. PD + LGD + EAD

B. PD × LGD × EAD

C. PD ÷ LGD × EAD

D. EAD − PD − LGD

Correct Answer: B. PD × LGD × EAD

Question 2

A loan has PD of 2%, LGD of 50%, and EAD of $10 million. What is expected loss?

A. $10,000

B. $50,000

C. $100,000

D. $200,000

Correct Answer: C. $100,000

Question 3

What does LGD primarily measure?

A. The probability of a downgrade

B. The exposure before a transaction is executed

C. The proportion of exposure lost after recoveries

D. The market value of collateral before a haircut

Correct Answer: C. The proportion of exposure lost after recoveries

Question 4

Which feature most directly reduces exposure by offsetting positive and negative trades with the same counterparty?

A. Credit migration

B. Close-out netting

C. Rating withdrawal

D. Spread duration

Correct Answer: B. Close-out netting

Question 5

Specific wrong-way risk exists when:

A. All counterparties have identical ratings

B. Exposure is directly linked to deterioration in the counterparty’s credit quality

C. A bank has no collateral agreement

D. A bond changes from fixed to floating rate

Correct Answer: B. Exposure is directly linked to deterioration in the counterparty’s credit quality

Question 6

In the simplified SA-CCR structure, EAD is based primarily on:

A. Only the original notional amount

B. Replacement cost and potential future exposure, scaled by a regulatory factor

C. Only historical default frequency

D. Accounting profit and loss

Correct Answer: B. Replacement cost and potential future exposure, scaled by a regulatory factor

Question 7

What is the principal purpose of CVA?

A. To adjust a derivative value for counterparty default risk

B. To eliminate all market risk

C. To calculate the borrower’s tax liability

D. To set the collateral haircut by accounting rule

Correct Answer: A. To adjust a derivative value for counterparty default risk

Question 8

A collateral asset with market value $8 million has a 10% haircut. What risk-reducing collateral value is recognised under the simplified haircut calculation?

A. $0.8 million

B. $7.2 million

C. $8.0 million

D. $8.8 million

Correct Answer: B. $7.2 million

Question 9

Which statement about credit migration is correct?

A. It refers only to legal enforcement after default

B. It can cause market-value losses before an actual default

C. It always improves portfolio diversification

D. It is identical to collateral liquidation

Correct Answer: B. It can cause market-value losses before an actual default

Question 10

Why is legal enforceability important for netting and collateral?

A. It determines whether contractual offsets and security interests will be recognised after default

B. It guarantees that default cannot occur

C. It removes the need for exposure measurement

D. It fixes the counterparty’s external rating

Correct Answer: A. It determines whether contractual offsets and security interests will be recognised after default