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FRM Part II Updated
What is economic capital, how does it differ from regulatory capital, and why do banks calculate both?
Economic capital is the bank's internal estimate of capital needed to absorb unexpected losses at a chosen confidence level. It differs from regulatory capital in risk coverage, correlation assumptions, and diversification treatment, and is the foundation for risk-adjusted performance measurement.
How is credit unexpected loss calculated, and what is its relationship to economic capital?
Unexpected loss measures the volatility of credit losses around expected loss. It captures both default uncertainty and LGD variability, and forms the basis for economic capital requirements since banks must hold capital to absorb losses beyond expected levels.
How do netting agreements reduce counterparty exposure and how is netting set exposure calculated?
A netting set is a group of trades with the same counterparty under a single master netting agreement. Netting allows you to offset positive and negative MTM values, dramatically reducing counterparty exposure compared to gross exposure calculation.
How do you construct the loss distribution for a credit portfolio, and what is the difference between expected and unexpected loss?
Building the credit portfolio loss distribution is the core challenge of credit risk management. Expected loss is straightforward to compute; the difficulty lies in capturing how losses cluster due to default correlations.
What makes a risk measure 'coherent,' and why does Expected Shortfall satisfy the criteria while VaR does not?
A coherent risk measure satisfies four axioms: monotonicity, subadditivity, positive homogeneity, and translation invariance. VaR fails subadditivity — combining portfolios can paradoxically increase measured risk — while Expected Shortfall satisfies all four axioms.
How does the Vasicek single-factor model work for credit portfolio loss estimation, and what is the granularity adjustment?
The Vasicek single-factor model is the theoretical foundation for Basel's IRB capital formula. It models each obligor's default as depending on a common systematic factor and an idiosyncratic factor, producing a portfolio loss distribution that captures correlated defaults during economic downturns.
What is component VaR and how does it decompose portfolio risk?
Component VaR decomposes total portfolio VaR into additive contributions from each position: CVaRᵢ = βᵢ × wᵢ × Portfolio VaR. Unlike individual VaRs, component VaRs sum exactly to portfolio VaR, revealing which positions contribute most to risk.
What are the main credit risk mitigation techniques and how do they reduce exposure?
Credit risk mitigation techniques include collateral (reduces LGD/EAD), netting (reduces gross exposure), guarantees (substitutes the guarantor's credit quality), and credit enhancements (subordination, overcollateralization). Basel recognizes specific CRM techniques for regulatory capital relief.
What are the main credit scoring model approaches and how does logistic regression compare to machine learning methods?
Credit scoring models assign a numerical score representing the probability that a borrower will default. There are several major approaches including logistic regression, decision trees, and neural networks, each with distinct trade-offs in accuracy versus interpretability.
What are the main pitfalls of historical simulation VaR and how do ghost effects distort results?
Historical simulation VaR has several pitfalls: the ghost effect (VaR jumps when extreme observations enter/exit the rolling window), inability to extrapolate beyond the worst historical loss, slow reaction to regime changes due to equal weighting, and regime dependence. Filtered HS and age-weighted approaches mitigate these issues.
How does the KMV model improve on Merton, and what is the Expected Default Frequency?
The KMV model improves on Merton by using an empirical default point (short-term debt plus half of long-term debt) and mapping distance to default to actual default frequencies from a historical database rather than the theoretical normal distribution, which systematically underestimates defaults.
Under FRTB, how does a bank decide between the Standardized Approach and Internal Models Approach for market risk capital?
Under FRTB, the choice between Standardized and Internal Models Approach is made at the trading desk level. Each desk must pass regulatory approval, P&L attribution testing, and backtesting to qualify for IMA. Desks that fail any gate must use the Standardized Approach.
What is wrong-way risk in counterparty credit, and why does it make standard CVA models underestimate losses?
Wrong-way risk occurs when your exposure to a counterparty increases at the same time their credit quality deteriorates. This positive correlation means standard CVA models that assume independence between exposure and default probability will systematically underestimate losses.
How do you calculate Expected Loss from PD, LGD, and EAD, and why does each component matter separately?
Understanding the three pillars of expected loss is essential for FRM Part II credit risk. Banks estimate PD, LGD, and EAD independently because each is driven by different factors: PD reflects borrower creditworthiness, LGD depends on collateral and seniority, and EAD accounts for potential drawdowns on revolving facilities.
What is the modern university endowment asset allocation?
Modern large endowments maintain 60%+ alternatives with diversified private equity, real assets, venture, and a liquidity sleeve.
How do banks conduct liquidity stress tests and what scenarios do they typically model?
Liquidity stress testing simulates severe but plausible funding disruptions to determine whether the bank can survive without external support. Banks typically model three scenarios: idiosyncratic stress, market-wide stress, and a combined scenario, each with different assumptions about deposit runoff, wholesale funding rollover, and asset liquidation discounts.
How does scenario analysis work for operational risk, and how do banks combine it with historical data?
Scenario analysis bridges the gap between historical loss data and the need to capitalize against catastrophic operational failures. Banks run structured expert workshops to estimate frequency and severity of plausible but severe events, then calibrate these against internal and external loss data.
Why does Basel III include a leverage ratio when we already have risk-weighted capital ratios?
The leverage ratio exists precisely because risk-weighted ratios can be gamed or miscalibrated. Before the 2008 crisis, many banks showed healthy CET1/RWA ratios while running actual leverage of 30:1 or higher by loading up on assets with low risk weights.
What is the TNFD framework, and how does nature-related financial risk extend beyond climate risk?
The TNFD framework addresses nature-related financial risks beyond climate, including biodiversity loss, ecosystem degradation, and resource depletion. Using the LEAP approach, organizations map nature interfaces, assess dependencies and impacts, and prepare disclosures across governance, strategy, risk management, and metrics.
How does the CCP loss allocation waterfall work when a clearing member defaults, and what happens if losses exceed the default fund?
The CCP loss allocation waterfall absorbs losses sequentially: first the defaulter's margin and default fund contribution, then the CCP's own equity, then non-defaulting members' contributions, then assessment powers. If all layers are exhausted, the CCP may haircut variation margin gains or tear up contracts.
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