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What is the Comprehensive Risk Measure (CRM), and why was it created specifically for correlation trading portfolios?
The Comprehensive Risk Measure was a Basel 2.5 capital charge for correlation trading portfolios like CDO tranches, capturing default correlation changes, tranche leverage, and recovery uncertainty at 99.9% confidence over one year. Under FRTB, it was eliminated and replaced by longer-horizon ES and DRC.
What is the Anderson-Darling test, and why is it preferred over Kolmogorov-Smirnov for testing normality in risk management applications?
The Anderson-Darling test is a goodness-of-fit test that places extra weight on the tails of the distribution, making it superior to the Kolmogorov-Smirnov test for detecting fat tails in financial return data. This matters because risk managers care most about tail behavior.
How do you read a QQ plot to assess whether financial return data follows a normal distribution?
A QQ plot compares observed data quantiles against theoretical normal quantiles. If points fall on the diagonal, the data is normal. Fat tails appear as S-shaped deviations, indicating that normal-distribution VaR underestimates tail risk.
How does the Red/Amber/Green model validation framework work?
The RAG framework classifies model validation findings into Red (critical, immediate remediation), Amber (significant, 6-12 month fix), and Green (minor, next cycle). Classification is based on materiality of impact on capital, P&L, or risk limits.
How does a longevity swap work and why do pension funds use them?
A longevity swap transfers the risk that a group of people live longer than expected from a pension fund to a counterparty. The pension fund pays a fixed leg based on expected mortality and receives a floating leg based on actual mortality.
How do you compute parametric VaR when returns are non-normal? Is there a Cornish-Fisher adjustment?
The Cornish-Fisher expansion modifies the standard normal quantile to account for skewness and excess kurtosis, producing a more accurate VaR estimate without abandoning the parametric approach.
How do you use the chi-squared test to test a hypothesis about variance?
The chi-squared test for variance is a natural extension of hypothesis testing to the second moment of a distribution. While z-tests and t-tests focus on the mean, the chi-squared test focuses on dispersion — critically important in risk management.
How does the Cornish-Fisher expansion adjust VaR for non-normality, and when should you use it?
The Cornish-Fisher expansion adjusts the normal z-score for skewness and kurtosis, providing a quick analytical VaR correction. It works well for moderate non-normality but breaks down at extreme confidence levels or very fat tails.
How do banks estimate exposure at default (EAD), especially for off-balance-sheet commitments?
EAD for unfunded commitments includes expected drawdowns: EAD = Drawn + CCF x Undrawn. Credit conversion factors reflect the empirical observation that distressed borrowers draw down credit lines before defaulting.
How do jump-diffusion models improve on geometric Brownian motion for risk modeling?
Jump-diffusion models add sudden, discrete price changes to the continuous GBM framework. This produces fat tails and negative skewness, matching real market behavior far better than Normal-distribution models.
What exactly is a variance swap and how does the payoff work relative to implied volatility?
A variance swap is an OTC derivative where one party pays a fixed rate (the 'strike variance') and receives the realized variance of an underlying asset over the contract period. It gives pure exposure to volatility without delta-hedging complications.
What are the main stress testing frameworks used in bank risk management?
Stress testing is a forward-looking risk management tool that evaluates portfolio performance under severe but plausible adverse scenarios. The main frameworks include sensitivity analysis, historical scenario analysis, hypothetical scenario analysis, and reverse stress testing.
How does the Basel backtesting traffic light system work for validating VaR models?
The Basel backtesting traffic light system validates VaR models by counting exceptions over 250 trading days. Green zone (0-4 exceptions) means the model is accepted with a 3.0x multiplier. Yellow zone (5-9) triggers regulatory inquiry and higher multipliers. Red zone (10+) indicates model failure with a 4.0x multiplier.
How does insurance reduce operational risk capital under Basel rules?
Insurance mitigation under Basel's Advanced Measurement Approach allows banks to reduce operational risk capital by up to 20% to recognize genuine loss transfer, subject to strict eligibility criteria...
What happens in a pension buy-out transaction?
A buy-out transfers pension liability to an insurer via group annuity, while a buy-in holds the policy as a plan asset.
What is the supervisory slotting approach for specialized lending?
Slotting applies to specialized lending (PF, OF, CF, IPRE, HVCRE) where PD/LGD data is insufficient. Banks slot into 5 categories: Strong 70%, Good 90%, Satisfactory 115%, Weak 250%...
How do you bootstrap zero-coupon rates from coupon bond prices step by step?
Bootstrapping extracts zero-coupon spot rates from coupon bond prices by solving sequentially from the shortest maturity to the longest. Each step uses previously derived rates as inputs, building the complete term structure one maturity at a time.
What makes infrastructure a distinct alternative asset class? How do risk/return profiles compare to other alternatives?
Infrastructure stands out for its essential-service nature, regulated/contracted revenues, inflation linkage, and very long asset lives. It offers a risk-return profile between bonds and equity with strong income characteristics.
How is NLP used in finance? Can text data really predict stock movements?
NLP transforms unstructured text into quantitative signals for investment analysis. Key applications include sentiment analysis of earnings calls, topic modeling of Fed minutes, and detecting material changes in corporate filings.
What are the main theories on dividend policy and which one is 'correct' for the CFA exam?
Three major theories disagree on whether dividends matter. MM says irrelevant in perfect markets, bird-in-hand says investors prefer certainty of dividends, and tax preference says investors prefer capital gains if taxed at lower rates.
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