Credit Portfolio and Counterparty Risk

Introduction and Context

Credit risk is the possibility that a borrower, issuer, guarantor, or trading counterparty fails to meet contractual obligations, causing financial loss. At the individual-exposure level, credit risk is concerned with the quality and recoverability of a loan or security. At the portfolio level, the central question is different: how do many exposures behave together under normal and stressed economic conditions?

A bank may have thousands of apparently diversified loans but still be vulnerable because borrowers depend on the same industry, region, commodity price, interest-rate environment, or supply chain. Portfolio credit risk therefore combines borrower-specific uncertainty with systematic risk, dependence, concentration, and the timing of losses. Counterparty credit risk adds a further complication: exposure may change before default, particularly in derivatives, securities financing, and repurchase transactions.

Sound credit-risk management integrates measurement, pricing, limits, provisioning, capital allocation, stress testing, collateral management, legal documentation, and governance. The objective is not to eliminate credit risk—an economically useful bank must accept credit risk—but to ensure that risk is understood, compensated, controlled, and resilient under adverse conditions.

Learning Objectives

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

1. Explain probability of default, loss given default, exposure at default, expected loss, and unexpected loss.
2. Calculate and interpret expected credit loss under alternative measurement assumptions.
3. Explain credit migration, transition matrices, concentration risk, default correlation, and portfolio diversification.
4. Analyze counterparty credit risk, potential future exposure, netting, collateral, and wrong-way risk.
5. Evaluate credit derivatives, securitization, and other credit-risk-transfer mechanisms.
6. Critically assess model risk, governance, regulation, ethics, and emerging technology in credit portfolios.

1. The Architecture of Credit Loss

1.1 Default and credit events

Default is a defined credit event rather than simply a late payment. A bank’s definition may include material payment delinquency, bankruptcy, restructuring on disadvantageous terms, or a determination that the obligor is unlikely to pay. The definition must be applied consistently for internal models, accounting, regulatory reporting, and performance analysis, although the precise legal and regulatory definitions can differ.

Credit deterioration can occur without default. A borrower may move from a high rating to a lower rating, increasing its spread and capital requirement. This is migration risk. Default risk is therefore only one part of credit risk; mark-to-market loss, downgrade, spread widening, and loss of liquidity may occur before contractual default.

1.2 PD, LGD, and EAD

The basic one-period expected-loss relationship is:

where:

• PD is the probability that the obligor defaults during the specified horizon;
• LGD is the loss severity as a proportion of exposure after recoveries and recovery costs; and
• EAD is exposure outstanding when default occurs.

The formula assumes that PD, LGD, and EAD are defined over the same horizon and that their product represents the probability-weighted loss. In a simple deterministic example, independence between the components is not required if the values are conditional or appropriately averaged. In more advanced models, dependence between default, exposure, and recovery must be represented explicitly.

LGD is commonly expressed as:

If recovery is measured net of legal, administrative, and realization costs, this identity is valid on a consistent economic basis. Recovery may arise from collateral, guarantees, restructuring proceeds, liquidation, or future cash flows. A secured loan does not necessarily have low LGD: collateral may be volatile, illiquid, overvalued, or legally difficult to enforce.

EAD is straightforward for a fully drawn term loan but uncertain for revolving facilities, overdrafts, guarantees, and derivatives. For an undrawn commitment, the bank often estimates a credit conversion factor (CCF):

The CCF is the expected proportion of unused commitment drawn by the time of default. It must be calibrated to the relevant product, borrower behaviour, economic conditions, and observation horizon.

1.3 Worked example: expected loss

A bank has a one-year corporate exposure of $20 million. The one-year PD is 2.5%, expected LGD is 40%, and EAD is $20 million.

The expected loss is therefore $200,000. This is an average loss estimate over many comparable exposures or repeated economic states; it is not a forecast that exactly $200,000 will be lost on this particular loan. The actual outcome is generally either no default loss or a substantially larger loss.

For a revolving facility with $8 million drawn and $5 million undrawn, a CCF of 60%, PD of 3%, and LGD of 45%:

1.4 Discounted cash-flow measurement

For an instrument measured using expected cash flows, expected credit loss can be represented as the present value of contractual cash flows less the present value of expected cash flows:

The discount rate, cash-flow timing, default assumptions, recoveries, and treatment of collateral must be consistent with the applicable accounting framework. A lifetime, scenario-weighted formulation is:

Here, s indexes economic scenarios, w_s is the probability weight of scenario s, t is the time period, DF_t is the discount factor, and T is the maturity or relevant measurement horizon. The weights must sum to one. This formulation is an analytical representation; actual accounting standards prescribe detailed rules concerning staging, forward-looking information, discounting, and reasonable supportable forecasts.

1.5 Expected and unexpected loss

Expected loss is normally addressed through pricing, provisions, and reserves. Unexpected loss is the variation around expected loss and is addressed primarily through economic and regulatory capital. A portfolio loss distribution illustrates the distinction: the mean represents expected loss, while a high percentile—such as a stressed or capital confidence level—captures severe but plausible loss.

A simplified capital concept is:

The expression is conceptual rather than a universal regulatory formula. The percentile must be specified, the portfolio loss distribution must be modelled, and diversification and dependence must be captured. Capital models may use value at risk, expected shortfall, regulatory formulas, or economic-capital simulations.

2. Credit Migration and Portfolio Dynamics

2.1 Migration risk

Credit ratings summarize relative credit quality. Migration is movement from one rating category to another over a specified period. A downgrade may cause a bond’s market value to fall even if no default occurs; an upgrade may create a gain. Loan portfolios are also affected because migration changes pricing, risk appetite, collateral requirements, and capital consumption.

A transition matrix contains probabilities of moving from an initial rating to a final rating. Each row should sum to one, subject to rounding. For example, a one-year matrix may show that an A-rated obligor has a 90% probability of remaining A, a 5% probability of becoming BBB, a 1% probability of default, and other probabilities of upgrading or moving to different categories.

Transition matrices are conditional on time horizon, rating system, sector, geography, and economic regime. Historical average matrices can be misleading when default rates are cyclical. Point-in-time matrices respond more quickly to current conditions; through-the-cycle matrices are more stable but may lag turning points.

2.2 Mark-to-market and default loss

For traded credit instruments, total credit loss may be decomposed into:

1. spread or migration loss before default;
2. default loss if the obligor fails; and
3. liquidity, basis, and market-interaction effects.

A loan held at amortized cost may not immediately recognize market spread movements, but the economic risk remains relevant for portfolio management, sale decisions, liquidity, and capital planning.

3. Concentration, Correlation, and Diversification

3.1 Concentration risk

Concentration risk arises when exposures share a common source of risk. Common dimensions include:

• single-name and connected-counterparty concentration;
• industry and sector concentration;
• geographic concentration;
• product, collateral, and maturity concentration;
• currency and interest-rate concentration;
• supply-chain and common-obligor concentration; and
• concentration in low-frequency, high-severity exposures.

A portfolio can satisfy a formal single-borrower limit while remaining highly concentrated economically. For example, loans to several legally separate construction firms may all depend on one property market and a small group of developers.

A basic exposure share is:

This is useful but incomplete. Risk-weighted exposure, expected loss, stressed loss, correlation, and capital contribution may be more informative than nominal exposure alone.

3.2 Default correlation

Default correlation measures the tendency of obligors to default together. It may arise from common macroeconomic factors, industry shocks, shared lenders, contagion, or direct contractual links. High default correlation reduces the benefit of diversification because losses cluster in the same states of the world.

For two default indicators, D₁ and D₂, default correlation is conceptually:

where D_i = 1 if obligor i defaults in the horizon and zero otherwise. Correlation is difficult to estimate because defaults are rare, observations are limited, and relationships change during stress. A low historical correlation is not proof of low future dependence.

3.3 One-factor portfolio intuition

Many portfolio models assume that an obligor’s asset value depends on a systematic factor and an idiosyncratic factor:

Here, Z is a common systematic factor, ε_i is obligor-specific risk, ρ_i is the asset correlation, and the factors are commonly assumed to be standardized and mutually independent except for the common factor relationship. Default occurs when X_i falls below an obligor-specific threshold. This framework explains why defaults are relatively independent in good conditions but cluster when the systematic factor deteriorates.

The model is an analytical simplification, not a description of all credit portfolios. Multiple factors, sector dependence, stochastic recovery, contagion, and nonlinear exposures may be required.

3.4 Portfolio diversification

Diversification reduces idiosyncratic risk when exposures have imperfect dependence. It does not eliminate systematic risk. Effective diversification requires economic—not merely legal—independence. A bank should examine marginal and incremental risk contributions: how much does a proposed exposure add to portfolio expected loss, stress loss, volatility, or capital?

A useful managerial principle is that the relevant question is not “Is this borrower individually acceptable?” but “What does this borrower do to the entire portfolio under normal and stressed conditions?”

4. Counterparty Credit Risk

4.1 Definition and distinctive features

Counterparty credit risk is the risk that a trading or financial counterparty defaults before the final settlement of a transaction and that the bank has a positive replacement cost. Unlike a conventional loan, exposure can be positive, zero, or negative and can change daily with market prices.

Products creating counterparty exposure include swaps, forwards, options, securities financing transactions, repurchase agreements, securities lending, and unsettled trades. A derivative may have no current exposure at inception but substantial potential future exposure if market variables move.

4.2 Current and potential exposure

Positive current mark-to-market is often called replacement cost. A simplified unsecured exposure is:

where MTM_t is the transaction’s mark-to-market value from the bank’s perspective at time t. This formula excludes collateral and legal netting and therefore applies only as a simplified gross measure.

Potential future exposure (PFE) estimates possible future replacement cost over a specified confidence level and horizon. A stylized exposure measure is:

where V_t is portfolio value, C_t is eligible collateral after applicable adjustments, T is the time horizon, and E denotes expectation under the exposure model. EPE is an average expected positive exposure; it is not the same as a high-quantile PFE. In practice, exposure models incorporate volatility, correlations, optionality, margining, collateral disputes, legal terms, and simulation assumptions.

4.3 Netting

Close-out netting allows positive and negative positions with the same counterparty to be aggregated after a default, subject to enforceable legal documentation. If enforceable, netting can materially reduce exposure. Netting is not simply an accounting convenience: its value depends on jurisdiction, contract wording, insolvency law, and operational ability to terminate and value transactions.

A simplified net exposure after netting is:

This expression assumes that all transactions are legally nettable, valuations are simultaneous, and collateral is immediately enforceable. Those assumptions must be tested rather than presumed.

4.4 Collateral and margin

Collateral reduces loss if it is liquid, sufficient, enforceable, and available when needed. Relevant risks include:

• market-value volatility and haircuts;
• margin-period-of-risk, during which exposure may grow;
• settlement and operational failure;
• wrong-way collateral, such as collateral whose value falls when the counterparty weakens;
• disputes over valuation or eligibility; and
• rehypothecation and segregation risk.

A collateral haircut protects against liquidation-price uncertainty. If collateral has market value C and haircut h, recognized collateral is:

The haircut is expressed as a decimal. The formula does not capture currency mismatch, concentration, liquidation cost, or gap risk unless those are incorporated into the haircut framework.

4.5 Wrong-way risk

Wrong-way risk occurs when exposure to a counterparty increases as its credit quality deteriorates. General wrong-way risk arises from common macroeconomic factors; specific wrong-way risk arises from a direct relationship between the counterparty and the transaction. A put option on a company’s own shares, for example, may have exposure that rises when the company weakens. Wrong-way risk should be identified in onboarding, approved in limits, modelled conservatively, and stress tested.

5. Credit Risk Transfer

5.1 Guarantees and insurance

Guarantees and financial insurance transfer some loss to a guarantor, but the protection is only as strong as the guarantor’s ability and willingness to perform. Banks must assess guarantor PD, legal enforceability, maturity mismatch, currency, call conditions, and correlation between guarantor and borrower. A guarantor exposed to the same economic shock may fail when needed most.

5.2 Credit derivatives

A credit default swap (CDS) transfers defined credit risk from protection buyer to protection seller in exchange for a premium. The buyer generally pays a periodic spread and receives compensation after a specified credit event, subject to contract terms and settlement mechanics. CDS can hedge a reference exposure, create synthetic exposure, or support price discovery.

Important risks include basis risk between the hedged asset and reference entity, counterparty risk to the protection seller, restructuring-definition disputes, liquidity risk, maturity mismatch, and model risk. A hedge that reduces regulatory exposure may not eliminate economic loss if its value, trigger, or settlement differs from the underlying exposure.

5.3 Securitization

Securitization pools assets and issues claims with different seniority, often creating tranches. Cash flows from the asset pool are allocated according to a contractual waterfall. Credit enhancement may include subordination, excess spread, overcollateralization, reserve accounts, or guarantees.

Securitization can diversify funding and transfer risk, but it may create opacity, model dependence, servicing risk, adverse selection, and correlation risk. Seniority does not make a tranche risk-free. Stress analysis should examine default timing, recovery rates, prepayments, servicer failure, trigger breaches, and dependence among assets.

5.4 Retention and incentives

Risk transfer can weaken origination incentives if the originator retains little exposure or relies excessively on external ratings. Effective governance requires clear underwriting standards, transparent reporting, appropriate risk retention where required, investor due diligence, and monitoring after issuance. Risk transfer should be evaluated on a look-through basis: the bank must understand who ultimately bears the risk and whether protection remains effective under stress.

6. Measurement, Governance, and Regulation

6.1 Data and model governance

Credit models depend on accurate obligor identification, facility data, collateral records, repayment histories, defaults, recoveries, ratings, and legal documentation. Data lineage should show how source data becomes a model input and then a decision. Model governance should include conceptual soundness review, independent validation, performance monitoring, override controls, change approval, and documented limitations.

PD models may use logistic regression, survival analysis, scorecards, machine learning, or structural approaches. LGD models should reflect collateral type, seniority, time to recovery, costs, and macroeconomic conditions. EAD models should capture utilization behaviour and contractual features. No algorithm compensates for biased, incomplete, or unstable data.

6.2 Stress testing

Stress testing examines portfolio behaviour under adverse but plausible conditions, such as recession, unemployment, falling property prices, commodity collapse, currency depreciation, or sovereign distress. Scenarios should affect PD, LGD, EAD, migration, collateral, funding, and correlations—not only one variable in isolation.

Reverse stress testing asks what combination of events would threaten capital, liquidity, covenant compliance, or viability. It is particularly useful for concentrated portfolios and portfolios with nonlinear derivatives.

6.3 Regulatory and accounting perspectives

Regulatory capital frameworks generally distinguish credit risk in banking-book exposures from counterparty credit risk in trading and derivatives activities. They use standardized and, subject to permission, internal-model approaches. Accounting frameworks require impairment recognition using prescribed expected-credit-loss concepts, staging or similar deterioration criteria, forward-looking information, and scenario analysis. The precise treatment depends on the applicable jurisdiction and standard; institutions must not assume that accounting ECL, regulatory capital EL, and economic capital are identical.

Governance should include a board-approved risk appetite, independent risk management, credit committees, limit systems, escalation triggers, internal audit, and clear accountability. A limit is effective only when exposures are measured accurately, breaches are detected promptly, and escalation has consequences.

7. Technology, AI, and Emerging Practice

Automation can improve early-warning systems by combining payment behaviour, financial statements, transaction patterns, supply-chain information, and public data. Machine learning may detect nonlinear interactions and weak signals, but it introduces explainability, stability, privacy, cybersecurity, and discrimination concerns. A high-performing model in ordinary conditions may fail under a regime change.

Best practice is to use AI as part of a controlled decision architecture rather than as an unreviewed replacement for credit judgment. Institutions should maintain feature governance, validation, drift monitoring, human review for material decisions, documentation of rejected applications, fairness testing, and an override rationale. Synthetic data and alternative data require particular scrutiny because apparent volume does not ensure economic representativeness.

Climate and environmental risks increasingly affect PD, LGD, EAD, and correlation. Physical hazards can damage collateral and disrupt borrowers; transition policies can reduce the value of carbon-intensive assets. These risks should be translated into borrower cash flows, collateral valuations, sector limits, and scenario analysis rather than treated only as qualitative disclosures.

8. Illustrative Case: Meridian Bank’s Commercial Portfolio

Background

Meridian Bank has a $1 billion commercial-credit portfolio: 35% real estate, 25% energy services, 20% manufacturing, and 20% diversified small and medium-sized enterprises. Individually, most borrowers meet internal approval standards. However, real estate and energy-service borrowers are linked through regional employment, construction demand, and bank collateral values.

Problem

A downturn scenario combines a 15% decline in commercial property prices, lower energy prices, and a recession. Relationship managers report that several borrowers are drawing unused revolving facilities. The portfolio’s historical default correlation appears low because the sample contains few severe downturns.

Analysis

The bank first maps connected counterparties and common risk drivers. It recalculates EAD using higher CCFs for revolving facilities, revalues collateral using stressed prices, and applies sector-specific PD and LGD assumptions. It then runs a multi-factor simulation rather than relying on independent-obligor assumptions. The analysis shows that expected loss rises moderately, but the 99th-percentile loss rises sharply because defaults and collateral declines cluster.

Counterparty exposures in energy-derivative transactions are also reviewed. Netting agreements are enforceable for most, but not all, legal entities. One major energy client has collateral whose value is positively related to the client’s own solvency; this is identified as specific wrong-way risk.

Approach and solution

The credit committee approves a package of measures:

1. temporary limits on new commercial real estate and energy-service exposures;
2. reduced limits for connected groups rather than only individual legal entities;
3. higher collateral haircuts and more frequent margin calls;
4. targeted covenant monitoring and early-warning triggers;
5. hedging of selected energy exposures, with protection-seller limits assessed separately;
6. capital allocation based on stressed incremental risk; and
7. a board escalation if stressed loss exceeds the risk-appetite threshold.

Outcome

The portfolio remains within solvency and liquidity tolerances, but profitability declines because the bank prices risk and holds additional capital. The exercise also reveals weaknesses in legal-entity mapping, collateral data, and historical recovery information. Meridian therefore funds data remediation rather than treating the stress result as merely a limit problem.

Learning points

The case demonstrates that individual credit quality can conceal portfolio vulnerability. Concentration is economic as well as legal; EAD rises during stress; collateral may become less valuable exactly when needed; and low observed correlation may reflect insufficient adverse observations. Portfolio governance must connect models, limits, documentation, pricing, and human escalation.

Challenges and Limitations

Credit-risk measurement is subject to parameter uncertainty, structural breaks, survivorship bias, missing defaults, changing underwriting standards, and procyclical behaviour. PD estimates may be understated during expansions; LGD models may rely on collateral values that are themselves cyclical; EAD models may fail when borrowers draw facilities defensively. Dependence assumptions are particularly fragile because joint defaults are rare in ordinary data but decisive in crises.

Counterparty models face additional challenges from complex derivatives, netting sets, collateral disputes, intraday exposure, margin calls, and modelled future market paths. Legal enforceability cannot be inferred from a mathematical exposure simulation. A technically sophisticated model can produce false confidence when contract data or scenario design is weak.

Ethically, banks must avoid discriminatory use of data, opaque automated decisions, irresponsible lending, and risk transfer that merely conceals exposure. Customers should receive fair treatment, material decisions should be explainable, and governance should protect the financial system from the collective consequences of individually rational actions.

Best-Practice Framework

A mature credit-portfolio and counterparty-risk programme should:

• define default, exposure, recovery, and connected-counterparty relationships consistently;
• maintain forward-looking PD, LGD, EAD, migration, and correlation analysis;
• combine borrower-level underwriting with portfolio-level risk appetite;
• apply enforceable netting and collateral controls;
• distinguish current exposure, PFE, EPE, stress exposure, and capital measures;
• monitor wrong-way risk and concentration across legal and economic dimensions;
• validate models independently and challenge assumptions;
• use stress and reverse-stress testing regularly;
• assess credit-risk-transfer instruments on a look-through basis; and
• ensure board oversight, documented accountability, data quality, and ethical use of technology.

Chapter Summary

Credit portfolio risk is the distribution of losses across many exposures and economic states. The central building blocks are PD, LGD, and EAD, whose product provides a simplified expected-loss estimate. ECL may be scenario weighted and discounted, while unexpected loss concerns variation beyond the expected amount and is relevant to capital.

Migration risk captures deterioration or improvement before default. Concentration and default correlation determine whether diversification is genuine or illusory. Portfolio analysis therefore requires common-factor, sector, geography, collateral, and connected-counterparty perspectives.

Counterparty credit risk differs from ordinary lending because exposure changes with market values. Current exposure, PFE, EPE, netting, collateral, margin-period-of-risk, and wrong-way risk must be analysed together and supported by enforceable documentation. Guarantees, CDS, securitization, and other transfers can reduce risk but introduce basis, legal, counterparty, liquidity, incentive, and model risks.

Effective management joins quantitative models to governance: reliable data, validation, stress testing, limits, capital, accounting, legal review, human judgment, and ethical controls. AI and alternative data may improve prediction, but only when their stability, fairness, explainability, and performance under stress are actively governed.

Key Terms

Probability of Default (PD): Probability that an obligor defaults over a specified horizon.

Loss Given Default (LGD): Proportion of exposure lost after recoveries and costs.

Exposure at Default (EAD): Exposure outstanding when default occurs.

Expected Loss (EL): Probability-weighted average credit loss.

Expected Credit Loss (ECL): Present-value or scenario-weighted estimate of expected credit loss under the applicable framework.

Unexpected Loss: Loss variation beyond expected loss, relevant to capital.

Credit Migration: Movement between credit-quality or rating categories.

Transition Matrix: Matrix of probabilities of movement between rating states.

Concentration Risk: Risk arising from excessive exposure to a common source of loss.

Default Correlation: Tendency of obligors to default together.

Diversification: Reduction of idiosyncratic risk through imperfectly related exposures.

Counterparty Credit Risk: Risk of loss from a counterparty default before settlement.

Potential Future Exposure (PFE): Estimate of possible future positive exposure at a specified confidence level.

Expected Positive Exposure (EPE): Time-averaged expected positive exposure.

Netting: Contractual offsetting of transactions with the same counterparty.

Collateral Haircut: Reduction applied to collateral value to protect against liquidation and market risk.

Wrong-Way Risk: Risk that exposure increases as counterparty credit quality deteriorates.

Credit Default Swap (CDS): Derivative transferring defined credit-event risk for a premium.

Securitization: Funding or risk transfer through pooling assets and issuing tranched claims.

Credit Conversion Factor (CCF): Estimated proportion of an undrawn commitment drawn before default.

Review Questions

Short Answer Questions

1. Define PD, LGD, and EAD and explain why their time horizons must be consistent.
2. What is the difference between expected loss and unexpected loss?
3. Why can a portfolio be legally diversified but economically concentrated?
4. Distinguish current counterparty exposure from potential future exposure.
5. Explain specific wrong-way risk and give one example.

Long Answer Questions

1. Explain how migration, default, concentration, and correlation interact in portfolio credit-risk management.
2. Discuss the role of netting and collateral in counterparty credit risk, including their legal and operational limitations.
3. Compare guarantees, credit default swaps, and securitization as credit-risk-transfer mechanisms.
4. Critically evaluate the assumptions and limitations of a one-factor credit-portfolio model.
5. Design a governance framework for PD, LGD, EAD, ECL, and counterparty-exposure models.

Analytical / Application Questions

1. A facility has $12 million drawn, $8 million undrawn, a CCF of 50%, PD of 4%, and LGD of 35%. Calculate EAD and one-year expected loss.
2. A bank’s commercial portfolio has low historical default correlation but high exposure to one region and one collateral type. Explain why historical diversification measures may understate stressed loss and propose three tests.
3. A derivatives portfolio has positive and negative transactions with one counterparty, but only some transactions are covered by an enforceable netting agreement. Explain how the bank should measure exposure and identify the principal legal, collateral, and wrong-way risks.

Multiple Choice Questions with Answers

Question 1

A loan has PD of 2%, LGD of 40%, and EAD of $10 million. What is its expected loss?

A. $8,000

B. $80,000

C. $200,000

D. $800,000

Correct Answer: B. $80,000

Question 2

Which component estimates the amount outstanding when a borrower defaults?

A. PD

B. LGD

C. EAD

D. CCF

Correct Answer: C. EAD

Question 3

What is the principal effect of high default correlation in a portfolio?

A. It increases diversification benefits

B. It causes losses to cluster

C. It eliminates systematic risk

D. It guarantees lower LGD

Correct Answer: B. It causes losses to cluster

Question 4

A facility has $6 million drawn and $4 million undrawn, with a CCF of 50%. What is EAD?

A. $5 million

B. $6 million

C. $8 million

D. $10 million

Correct Answer: C. $8 million

Question 5

Which statement best describes migration risk?

A. Risk that collateral is not legally owned

B. Risk of movement between credit-quality categories

C. Risk that a derivative is incorrectly settled

D. Risk caused only by borrower default

Correct Answer: B. Risk of movement between credit-quality categories

Question 6

Potential future exposure is primarily intended to measure:

A. Only the current positive mark-to-market

B. Possible future replacement cost

C. Historical recovery proceeds

D. The borrower’s annual interest expense

Correct Answer: B. Possible future replacement cost

Question 7

What is a necessary condition for netting to reduce recognized counterparty exposure?

A. All transactions must have positive value

B. Netting must be legally enforceable

C. Collateral must always be unsecured

D. The counterparty must have no derivatives

Correct Answer: B. Netting must be legally enforceable

Question 8

Wrong-way risk occurs when:

A. Exposure falls as the counterparty weakens

B. Exposure is unrelated to credit quality

C. Exposure rises as the counterparty weakens

D. Collateral is always greater than exposure

Correct Answer: C. Exposure rises as the counterparty weakens

Question 9

If collateral is worth $10 million and the haircut is 20%, what value is recognized before other adjustments?

A. $2 million

B. $8 million

C. $10 million

D. $12 million

Correct Answer: B. $8 million

Question 10

Which is a major limitation of historical default-correlation estimates?

A. Defaults are usually observed every day

B. Severe joint defaults are abundant in all samples

C. Defaults are rare and relationships can change in stress

D. Correlation has no effect on portfolio loss

Correct Answer: C. Defaults are rare and relationships can change in stress