Community Q&A
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FRM Updated
How do the threshold and minimum transfer amount in a CSA create residual unsecured exposure, and how is this quantified?
The threshold allows exposure up to its level without collateral, and the MTA prevents calls below a minimum amount. Together with the margin period of risk, the maximum unsecured exposure equals H + MTA + potential market move during the MPOR.
How was the ISDA fallback spread adjustment calculated when LIBOR ceased, and why was a spread needed at all?
The ISDA fallback spread adjustment compensates for the structural difference between LIBOR (unsecured term rate) and SOFR (secured overnight rate). It was calculated as the 5-year historical median of the daily LIBOR-SOFR spread, fixed as of March 5, 2021.
What is the Residual Risk Add-On under FRTB, and which exotic instruments are in scope?
The Residual Risk Add-On charges 0.1% or 1.0% of gross notional for instruments with residual risks not captured by standard sensitivity measures. It applies to exotic underlyings (weather, catastrophe) and other complex features (barriers, lookbacks, autocallables) with no netting allowed.
How does a quanto option eliminate currency risk, and what adjustment is made to the drift rate in pricing?
A quanto option eliminates currency risk by fixing the exchange rate at inception. Pricing requires a drift adjustment — reducing the foreign asset's drift by rho x sigma_S x sigma_X — which accounts for the correlation between the asset and the exchange rate.
How should banks govern machine learning models used in risk management, and what unique challenges do ML models pose for model validation?
ML model governance extends traditional model risk management by addressing interpretability challenges, automated feature engineering risks, and data distribution drift. Banks must implement post-hoc explainability tools, bias testing, champion-challenger deployment, and continuous monitoring with revalidation triggers.
What is the Hurst exponent, and how does it distinguish between mean-reverting, random, and trending time series?
The Hurst exponent quantifies whether a time series trends (H > 0.5), mean-reverts (H < 0.5), or follows a random walk (H = 0.5). Estimated via Rescaled Range analysis, it guides strategy selection: momentum for persistent series, mean-reversion for anti-persistent ones.
What are the key data and modeling requirements for estimating PD, LGD, and EAD under the Advanced IRB approach?
Under A-IRB, banks must estimate PD (5+ years data, through-the-cycle calibration), LGD (7+ years, downturn conditions), and EAD (5+ years, credit conversion factors for undrawn commitments). Each parameter has specific data requirements and calibration rules.
What is the difference between the Foundation IRB and Advanced IRB approaches under Basel, and who estimates which parameters?
Foundation IRB and Advanced IRB use the same risk-weight function but differ in which parameters the bank estimates. Under F-IRB, banks estimate only PD while LGD, EAD, and maturity use supervisory values. Under A-IRB, banks estimate all parameters, potentially achieving lower capital charges.
What is a spark spread, how is it calculated, and why is it important for power generation risk management?
The spark spread measures the gross profit margin for a gas-fired power plant by comparing electricity revenue to gas fuel cost. It equals the electricity price minus the product of the plant's heat rate and the gas price.
How does seasonality in natural gas create predictable patterns in the futures curve, and what risks does this create for hedgers?
Natural gas is one of the most seasonal commodity markets due to heating demand in winter and relatively steady production. The futures curve typically shows winter premiums reflecting storage economics, and the spread must cover storage, financing, and operational costs.
What is Funding Valuation Adjustment (FVA) and how does it relate to CVA/DVA?
FVA captures the cost or benefit of funding uncollateralized derivative positions at a rate above risk-free. It includes a Funding Cost Adjustment for positive exposures (a cost) and a Funding Benefit Adjustment for negative exposures (a benefit).
How do you use DV01 to hedge interest rate risk with swaps?
DV01 (Dollar Value of a Basis Point) measures how much the value of a position changes when interest rates shift by 1 basis point. For hedging with interest rate swaps, the goal is to match the DV01 of your exposure with the DV01 of the swap so that the combined position has near-zero rate sensitivity.
What are the risk retention rules for securitizations, and why do they require 'skin in the game'?
Risk retention rules were among the most important regulatory responses to the 2008 crisis. Post-crisis regulations require originators to retain at least 5% of the credit risk of securitizations they issue.
What is the difference between the premium leg and the protection leg of a CDS, and how do they determine the CDS spread?
A credit default swap has two legs, and understanding both is essential for FRM Part I credit risk questions. The premium leg is paid by the protection buyer as periodic spread payments, while the protection leg is the contingent payout on default.
How does the Standardized Measurement Approach (SMA) calculate operational risk capital under Basel III?
The Standardized Measurement Approach (SMA) calculates operational risk capital using a Business Indicator derived from financial statements, mapped through marginal coefficients that increase with bank size, and adjusted by an Internal Loss Multiplier that incorporates 10 years of historical loss experience.
What are CDX and iTraxx credit indices, and how are they used for portfolio hedging and trading?
CDX and iTraxx are standardized credit default swap indices referencing baskets of corporate entities. They provide liquid, tradeable exposure to broad credit risk but introduce basis risk when used to hedge single-name positions.
What is kernel density estimation and when should I use it instead of assuming a parametric distribution for risk modeling?
Kernel density estimation (KDE) is a non-parametric technique that estimates the probability density function by placing a smooth kernel at each observed data point and summing them. Unlike parametric methods, KDE lets the data determine the distribution shape.
How do catastrophe bonds work, and what are the different trigger types investors need to understand?
Catastrophe bonds (cat bonds) are insurance-linked securities that transfer catastrophic event risk from an insurer or reinsurer to capital market investors. If a qualifying catastrophe occurs, investors lose some or all of their principal, which goes to the sponsor to cover losses.
How does non-parametric (historical simulation) VaR work, and what are its strengths and weaknesses?
Historical simulation computes VaR directly from past returns with no distributional assumptions. It naturally captures fat tails and correlations, but suffers from backward-looking bias, ghost effects, and equal-weighting of all observations.
What's the difference between logistic regression credit scoring and the Altman Z-score, and when would you use each?
The Altman Z-score uses five fixed financial ratios to classify firms into safe, grey, or distress zones, while logistic regression directly estimates default probability using customizable predictors. Modern banks use logistic regression for Basel IRB models.
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