Skip to content

FRM Part II · FRM Exam Part II

Fundamentals of Credit Risk for FRM Part II

Fundamentals of credit risk is the set of tools that measure loss from a borrower's default: probability of default, loss given default, exposure at default, expected and unexpected loss, ratings, credit models, spreads and mitigation. To solve questions, identify the component asked, apply the formula, then interpret the result.

What this chapter covers

This chapter builds the vocabulary and arithmetic of credit risk. You start with the three building blocks: PD (probability of default), LGD (loss given default) and EAD (exposure at default). Together they give expected loss: EL = PD × LGD × EAD. From there you move to the loss distribution, where unexpected loss and credit VaR describe how bad losses can get beyond the average.

The middle of the chapter covers how default risk is estimated and modelled. Credit ratings and transition matrices give historical default and migration frequencies. Structural models (Merton type) link default to the firm's asset value and debt. Reduced-form models treat default as a random event with a hazard rate. You then compare risk-neutral default probabilities, implied by credit spreads, with real-world ones. The chapter ends with mitigation: collateral, netting and guarantees.

These ideas feed the rest of Part II. Credit portfolio and counterparty risk, capital under Basel, and structured credit all reuse PD, LGD, EAD and the loss distribution. Liquidity and current issues topics, such as private credit, also lean on this vocabulary. Get this chapter solid and later credit material gets easier.

Credit Risk Measurement and Management is one of the six topics in FRM Part II, and this chapter is its foundation. The exam has 80 multiple-choice questions in 4 hours, and many are applied: you are given numbers and must pick the right measure and interpretation. The calculations here are short and repeatable, so they are good marks if you practise. The concepts also carry into other credit readings, so a weak base here costs you marks in several places.

Fundamentals of Credit Risk: topics in the order to study them

  1. 1Credit Risk Components: PD, LGD, EAD and Expected LossEvery other topic uses these three inputs and the expected loss formula, so learn them first.
  2. 2Expected vs Unexpected Loss and Credit VaRIt extends expected loss to the loss distribution and shows why capital is held against unexpected loss.
  3. 3Credit Ratings and Transition MatricesRatings supply the practical source of PD and migration estimates that the loss measures need.
  4. 4Structural and Reduced-Form Credit ModelsAfter seeing observed data, you learn the two ways of modelling default theoretically.
  5. 5Credit Spreads and Risk-Neutral vs Real-World Default ProbabilitiesIt builds on the models to explain what market spreads imply and why they differ from historical PDs.
  6. 6Credit Risk Mitigation: Collateral, Netting and GuaranteesIt closes the chapter by showing how EAD and LGD are reduced, which you can only judge once you know the components.

How to prepare Fundamentals of Credit Risk

Aim for fluency in a few formulas and clear reasoning about what each measure means. Use short daily sessions that suit phone study.

  1. Write EL = PD × LGD × EAD from memory and solve five varied numerical examples, including ones where you are given recovery rate instead of LGD (LGD = 1 − recovery rate).
  2. Draw a loss distribution and mark expected loss, the credit VaR quantile and unexpected loss. Then state in one sentence how each is defined.
  3. Practise reading a transition matrix: find one-year default probability, probability of staying, and how to chain two years by multiplying matrices.
  4. Make a comparison sheet for structural and reduced-form models: inputs, what drives default, strengths and limits.
  5. Work through spread questions: approximate spread ≈ PD × LGD, and explain why risk-neutral PDs usually exceed real-world PDs.
  6. List each mitigation tool with what it reduces (EAD or LGD), its conditions and its residual risks, then do mixed MCQs and review every wrong answer.

Common mistakes in Fundamentals of Credit Risk

  • Mixing up LGD and recovery rate.

    Fix: Always write LGD = 1 − recovery rate before you multiply.

  • Treating expected loss and unexpected loss as the same thing.

    Fix: Remember expected loss is the mean, and unexpected loss is the variability beyond it. Capital covers the unexpected part.

  • Reading a transition matrix by column instead of by row.

    Fix: Check the labels. Rows are usually the starting rating and columns the ending rating, and each row sums to 1.

  • Using risk-neutral default probabilities as forecasts of actual default.

    Fix: State that spreads also carry compensation for risk and liquidity, so risk-neutral PDs are not real-world estimates.

  • Assuming mitigation removes credit risk.

    Fix: Name the residual risk each time: legal enforceability, collateral value changes, guarantor default and wrong-way risk.

Last-day revision: Fundamentals of Credit Risk

  • Expected loss = PD × LGD × EAD.
  • LGD = 1 − recovery rate.
  • Expected loss is the average loss; it is priced and provisioned.
  • Unexpected loss is the variability of loss around the expected loss, usually measured by standard deviation.
  • Credit VaR is a loss quantile at a chosen confidence level; it is often quoted relative to expected loss to give the unexpected part.
  • Transition matrix rows show migration probabilities from a starting rating and sum to 1.
  • Multi-year migration probabilities come from multiplying the matrix by itself, assuming the process is Markov.
  • Structural models tie default to asset value falling below debt; reduced-form models use a default intensity (hazard rate).
  • Approximate credit spread ≈ PD × LGD, under simplifying assumptions.
  • Risk-neutral PDs are usually higher than real-world PDs because spreads include a risk premium.
  • Netting reduces exposure only where it is legally enforceable.
  • Collateral and guarantees reduce loss but add risks, such as collateral value falling or guarantor default.

Fundamentals of Credit Risk practice questions

Fundamentals of 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.

Fundamentals of Credit Risk: frequently asked questions

How do I calculate expected loss in FRM Part II?

Multiply probability of default, loss given default and exposure at default: EL = PD × LGD × EAD. If you are given a recovery rate, convert it first using LGD = 1 − recovery rate. Check that PD and LGD are in the same decimal form.

What is the difference between expected and unexpected loss?

Expected loss is the average credit loss you anticipate and price into the loan. Unexpected loss is the uncertainty around that average. Capital is held mainly to absorb unexpected loss.

Do I need to memorise the Merton model in detail?

Focus on the idea and the interpretation. Equity behaves like a call option on the firm's assets, and default occurs when asset value falls below the debt due. Be ready to compare it with reduced-form models in words.

In what order should I study Fundamentals of Credit Risk?

Start with PD, LGD, EAD and expected loss, then the loss distribution and credit VaR. Next study ratings and transition matrices, then the two model types, then spreads, and finish with mitigation.