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FRM Part II · FRM Exam Part II

Credit Risk for FRM Part II: Chapter Study Guide

Credit Risk in FRM Part II covers how you measure and manage the chance that a borrower or counterparty fails to pay. You learn expected loss, ratings, default models, counterparty exposure, portfolio credit VaR, credit derivatives, securitization and Basel capital. Solve questions by naming the measure, applying the formula, then interpreting the result.

What this chapter covers

This chapter is about losses from borrowers and counterparties that fail to meet their obligations. It starts with the building blocks: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Expected loss = PD × LGD × EAD. From there it moves to how PD is estimated (scoring, ratings, structural and reduced-form models), and how exposure changes over time in derivatives (counterparty credit risk).

The second half scales up from one obligor to a portfolio. You look at correlation, concentration, unexpected loss and credit VaR. Then you see how risk is transferred or repackaged through credit derivatives and securitization. The chapter ends with Basel capital rules, which turn these measures into regulatory requirements.

Credit risk connects to the rest of the paper in many ways. Market risk tools such as VaR and simulation reappear in exposure and credit VaR. Liquidity and treasury risk link to funding and collateral. Operational risk links to model risk and data quality. Current Issues, such as private credit, uses the same default and loss ideas in new settings. Expect applied, case-like questions across all of these.

Credit Risk is one of the six topics in FRM Part II, and the exam is 80 equally weighted multiple-choice questions in 4 hours. The chapter has many separate sub-topics, so a gap in any one of them can cost you several questions. It also supports other topics, because the same PD, LGD, correlation and capital ideas appear in private credit, liquidity and investment management questions. Time spent here pays back across the whole paper, and the formulas are often quick marks once you know them well.

Credit Risk: topics in the order to study them

  1. 1Credit Risk Fundamentals and Expected LossStart here because PD, LGD, EAD and expected loss are used in every later topic.
  2. 2Credit Scoring and Rating SystemsNext you learn how PD is estimated in practice, including rating migration and model validation.
  3. 3Structural and Reduced-Form Default ModelsThese models give a theoretical link between firm value, spreads and default probability, building on PD estimation.
  4. 4Credit Exposure and Counterparty Credit RiskExposure in derivatives varies over time, so you need the single-obligor basics first before adding CVA, netting and collateral.
  5. 5Credit Portfolio Risk and Credit VaROnly after single-name measures can you add correlation, concentration and unexpected loss at portfolio level.
  6. 6Credit Derivatives and SecuritizationThese are tools for transferring risk, and they make most sense once you understand portfolio loss and default dependence.
  7. 7Credit Risk Capital and Basel FrameworkFinish with Basel, which turns the earlier measures into capital rules and ties the chapter together.

How to prepare Credit Risk

Aim to understand the logic of each measure, then drill applied questions. Many questions give a short case and ask you to pick the right measure or interpret a result.

  1. Read the topics in the study order above and write a one-page formula and definition sheet as you go.
  2. For each topic, ask three things: what risk is measured, how it is calculated, and what the number tells a risk manager.
  3. Practise expected loss and unexpected loss calculations until they take under a minute, including cases with multiple exposures.
  4. Compare model families side by side: structural versus reduced-form, and standardised versus internal ratings-based approaches. Note what each assumes and where it fails.
  5. For counterparty credit risk and Basel, learn exact terms such as CVA, netting, collateral, and risk-weighted assets, and what each does to exposure or capital.
  6. Do timed sets of multiple-choice questions mixed across the chapter, and review every wrong answer by naming the concept you missed.
  7. In the last week, connect the chapter to Current Issues such as private credit and to liquidity and market risk topics, and re-read your formula sheet.

Common mistakes in Credit Risk

  • Mixing up expected loss and unexpected loss, or saying capital covers expected loss.

    Fix: Remember that expected loss is priced and provisioned, while capital is held against unexpected loss.

  • Using recovery rate where LGD is needed, or the reverse.

    Fix: Write LGD = 1 − recovery before you calculate, every time.

  • Treating counterparty exposure as symmetric, so negative values offset losses.

    Fix: Use exposure = max(value, 0) per netting set, and apply netting only where the question states it applies.

  • Confusing structural and reduced-form models.

    Fix: Tie structural to firm value and debt thresholds, and reduced-form to hazard rates and unexpected default timing.

  • Assuming higher correlation raises portfolio expected loss.

    Fix: Correlation changes the shape of the loss distribution and the tail, not the sum of expected losses.

  • Learning Basel terms loosely and picking near-match options.

    Fix: Learn precise definitions as GARP presents them and practise questions that test one term against its close neighbours.

Last-day revision: Credit Risk

  • Expected loss = PD × LGD × EAD.
  • LGD = 1 − recovery rate, when recovery is expressed as a fraction of exposure.
  • Expected loss is a cost of doing business; unexpected loss is the volatility of losses around it and is what capital covers.
  • Structural models treat equity as a call option on firm assets and default as assets falling below debt.
  • Reduced-form models treat default as a random event with a hazard rate, often linked to credit spreads.
  • Counterparty exposure is positive-only: you lose only if the counterparty owes you net value at default.
  • Netting and collateral reduce exposure; netting works within legally enforceable agreements.
  • CVA is the market value of counterparty credit risk on derivatives.
  • Higher default correlation fattens the loss tail in a portfolio without changing expected loss.
  • Credit VaR is a loss quantile at a confidence level over a horizon, measured relative to expected loss or to zero depending on the definition used.
  • Securitization tranches absorb losses in order; equity takes first loss and senior tranches last.
  • Basel capital is based on risk-weighted assets, with standardised and internal ratings-based approaches.

Credit Risk practice questions

Credit Risk in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Credit Risk: frequently asked questions

How long should I spend on Credit Risk for FRM Part II?

Give it a large share of your study time because it has seven separate topics and links to other parts of the paper. Plan time for both learning and timed practice. Adjust based on your work background, since banking credit staff may need less time on fundamentals.

Is Credit Risk heavy on calculations?

There are calculations such as expected loss, exposure, and portfolio effects, but they are usually short. The harder part is choosing the right measure and interpreting the result. Practise both.

Do I need to memorise Basel details?

You need the main concepts and exact terms, such as risk-weighted assets and the approaches to measuring credit risk. Focus on what each rule is meant to do and how it changes capital, rather than on remembering every parameter.

How does this chapter connect to Current Issues?

The 2026 Current Issues readings include private credit, rising government debt and geopolitical risk. All of them use default, loss, exposure and spread ideas from this chapter. Knowing the fundamentals helps you reason through new case-style questions.