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FRM Updated
What is a total return swap and why do institutions use them instead of buying the reference asset directly?
A total return swap (TRS) is an OTC derivative where one party transfers the full economic performance of a reference asset — coupons, price appreciation, and depreciation — to the counterparty in exchange for a funding rate, typically SOFR plus a spread.
What is filtered historical simulation and how does it fix the problems of standard HS?
Filtered historical simulation combines GARCH volatility modeling with historical simulation. It filters returns by GARCH to extract standardized residuals, then rescales them by current volatility — producing VaR estimates that adapt immediately to changing conditions.
What's the difference between through-the-cycle (TTC) and point-in-time (PIT) PD, and why does it matter for capital?
PIT PD reflects current economic conditions and fluctuates with the business cycle, while TTC PD averages across the entire cycle and remains stable. The choice affects regulatory capital calculations, loan provisioning, and procyclicality.
How do AIC and BIC work for model selection, and when would they disagree?
AIC and BIC both penalize model complexity to prevent overfitting, but BIC applies a heavier penalty that grows with sample size. They disagree when a complex model offers modest fit improvement — AIC accepts extra parameters more readily than BIC.
Why do Eurodollar futures (and now SOFR futures) need a convexity adjustment when used for swap pricing?
The convexity adjustment is one of the trickiest concepts in fixed-income derivatives. Futures contracts are marked to market daily, creating a systematic bias that makes futures rates higher than equivalent forward rates.
How does factor-based risk decomposition work for market risk management?
Factor-based risk decomposition breaks portfolio VaR into contributions from identifiable market factors (equity, rates, FX, volatility) plus idiosyncratic risk. This reveals WHY a portfolio has risk, enabling targeted hedging, factor-level limits, and stress testing.
What is CVA and how does DVA work as a bilateral credit adjustment?
CVA is the market value of counterparty default risk (expected loss from their default), while DVA captures the value of your own default risk to the counterparty. The bilateral adjustment is: Value = Risk-Free - CVA + DVA. DVA is controversial because it creates gains when your own credit deteriorates.
What are AR and MA models and when do you use each for financial time series?
AR models relate the current value to its own past values (persistent patterns), while MA models relate it to past error terms (shock effects). AR is identified by PACF cutoff, MA by ACF cutoff. ARMA combines both for more complex patterns.
What are longevity swaps and how do pension funds use them?
Longevity swaps allow pension funds to exchange fixed payments (based on expected mortality) for floating payments (based on actual mortality), transferring the risk that beneficiaries live longer than projected to reinsurers or capital market investors.
Why does default correlation matter so much for credit portfolio losses?
Default correlation measures the tendency for multiple obligors to default together. While it doesn't change expected losses, it dramatically affects the tail of the loss distribution, making Credit VaR and economic capital extremely sensitive to correlation assumptions.
How does the FRTB internal models approach work and what is desk-level approval?
FRTB's internal models approach requires desk-level approval through backtesting and P&L attribution tests. Desks failing either test revert to the standardized approach. Capital is calculated using 97.5% Expected Shortfall with liquidity-adjusted horizons, replacing the previous 99% VaR framework.
How do CCPs reduce systemic risk and what happens when a clearing member defaults?
CCPs interpose themselves between derivative counterparties through novation, absorbing counterparty risk. When a member defaults, losses are covered through a waterfall: defaulter's margin, defaulter's default fund, CCP capital, then mutualized default fund. However, CCPs themselves concentrate systemic risk.
How is PCA used to decompose yield curve risk into principal components?
PCA decomposes yield curve movements into three main factors: level (parallel shift, ~88% of variance), slope (steepening/flattening, ~8%), and curvature (butterfly, ~3%). This reduces a high-dimensional risk problem to three independent, interpretable factors.
What's the intuition behind barrier option pricing and when are knock-ins cheaper than vanillas?
Barrier options activate (knock-in) or deactivate (knock-out) when the underlying hits a specified level. The key pricing relationship is in-out parity: knock-in plus knock-out equals the vanilla option price. Knock-ins are cheaper because they only have value along specific price paths.
How does a bank conduct liquidity stress testing, and what are the key scenarios?
Liquidity stress testing simulates how a bank's liquidity evolves under adverse scenarios, modeling cash outflows, inflows, and asset monetization to determine the survival horizon. Banks test idiosyncratic, market-wide, and combined scenarios, connecting results to contingency funding plans and regulatory metrics like LCR and NSFR.
What is the Loss Distribution Approach for operational risk, and how do Key Risk Indicators fit in?
The Loss Distribution Approach models operational risk by separately estimating frequency (how many loss events per year) and severity (how large each loss is), then combining them via Monte Carlo simulation. Key Risk Indicators complement LDA by providing forward-looking early warning signals that help management intervene before losses materialize.
How can a pension plan hedge longevity risk?
Longevity swaps, buy-ins, and reinsurance hedge the risk that pensioners live longer than actuarial assumptions predict.
What are the three main VaR calculation methods and when should each be used?
Value at Risk can be calculated using three methods: parametric (variance-covariance), historical simulation, and Monte Carlo simulation. Each has distinct strengths — parametric is fast for linear portfolios, historical simulation captures fat tails, and Monte Carlo handles non-linear instruments like options.
Parametric VaR vs. Historical Simulation VaR — when does each method fail?
Excellent question — understanding when VaR methods break down is arguably more important than knowing the formulas, and GARP loves testing this on the exam. Parametric VaR fails with non-normal returns, non-linear positions, and unstable correlations. Historical simulation suffers from ghost effects, backward-looking bias, and limited tail data.
How do sustainability-linked loans work, and what makes their margin ratchet mechanism different from green bonds?
Sustainability-linked loans adjust the borrower's interest rate based on achievement of preset ESG performance targets. Unlike green bonds, SLL proceeds are unrestricted. The margin ratchet mechanism provides direct financial incentives, with safeguards against gaming through materiality requirements and third-party verification.
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