Basel III Endgame FRTB & ISDA SIMM v2.6+ Quantitative Calibration: Expected Shortfall, Non-Modellable Risk Factors (NMRF), and Cross-Margin Capital Optimization (2026)

1. Executive Summary: The Basel III Endgame & Cross-Margin Paradigm Shift (2025–2026)#

The global prudential and margin architecture governing institutional derivatives trading books, central clearing interfaces, and non-centrally cleared bilateral over-the-counter (OTC) portfolios has reached a decisive structural inflection point across the 2025–2026 supervisory cycle. Under the consolidated Basel Committee on Banking Supervision (BCBS) Minimum Capital Requirements for Market Risk framework (MAR10 through MAR33) [1] and the Standardized Approach for Counterparty Credit Risk (CRE52 / SA-CCR) [2], global systemically important banks (G-SIBs), regional dealers, and quantitative hedge funds confront an unprecedented convergence between regulatory market risk capital, counterparty exposure multipliers, and bilateral initial margin (IM) funding obligations.

Historically, trading desk capital allocation under Basel 2.5 relied on a 10-day, 99th percentile Value-at-Risk ($VaR_{99\%,10d}$) combined with an additive Stressed VaR ($SVaR$) and Incremental Risk Charge ($IRC$). That legacy specification exhibited three fatal structural deficiencies during periods of acute market illiquidity:

1. Inability to Capture Tail Convexity: As a quantile boundary metric, $VaR_{99\%}$ ignored the magnitude and distribution of losses beyond the 99th percentile threshold, creating perverse incentives to accumulate deep out-of-the-money (OTM) gamma and correlation skew risks.
2. Uniform 10-Day Liquidation Assumption: Assuming all trading book exposures could be delta-hedged or unwound within 10 business days without inducing endogenous market impact violated empirical microstructure behavior across credit correlation, emerging market currencies, and bespoke volatility surfaces.
3. Permeable Diversification Parameters: Unrestricted empirical correlation matrices permitted banks to offset idiosyncratic risk factors across disparate asset classes, significantly understating aggregate portfolio drawdown during cross-asset correlation regime breaks.

To remediate these vulnerabilities, the Fundamental Review of the Trading Book (FRTB) replaces $VaR{99\%}$ with a liquidity-adjusted **97.5% Expected Shortfall ($ES{97.5\%}$) metric calibrated to the institution's most severe 12-month historical stress window (MAR33.5), enforces a five-tier Liquidity Horizon ($LH_j \in \{10, 20, 40, 60, 120\}$ days) scaling cascade (MAR33.12), and introduces two stringent desk-level gates: the Profit and Loss Attribution Test (PLAT) (MAR32.20) and the Risk Factor Eligibility Test (RFET) (MAR31.12). Any risk factor failing to demonstrate at least 24 verifiable real-price observations per year—or 100 observations over a 12-month period—is carved out of the diversifiable Expected Shortfall engine and subjected to an un-diversifiable Non-Modellable Risk Factor ($NMRF$)** stress scenario capital charge (MAR33.16).

Simultaneously, the uncleared derivatives market operates under the ISDA Standard Initial Margin Model (ISDA SIMM v2.6 / v2.7) [6], which computes bilateral initial margin using a variance-covariance sensitivity aggregation calibrated to a 99% confidence interval over a 10-day Margin Period of Risk ($MPOR$). When combined with the 72.5% Standardized Output Floor (RBC20), SA-CCR ($\alpha = 1.4$ supervisory multiplier), and the resulting Margin Valuation Adjustment ($MVA$) and Capital Valuation Adjustment ($KVA$) funding drags, quantitative risk engineering teams must solve a coupled, multi-regime optimization problem across every execution venue and clearinghouse.

Institutional risk officers evaluating cross-jurisdictional supervisory transposition can audit the primary rulebooks via our Global Regulatory Directory, including the Basel Committee on Banking Supervision (BCBS), the Federal Reserve Board (FRB), the European Banking Authority (EBA), the UK Financial Conduct Authority & PRA, the Swiss Financial Market Supervisory Authority (FINMA), and the Monetary Authority of Singapore (MAS), alongside our institutional execution counterparty benchmarks in the Prime & Multi-Asset Broker Comparison Matrix.

2. Mathematical Architecture of FRTB Internal Models Approach (IMA) & 97.5% Expected Shortfall (MAR33)#

2.1 Continuous-Time Tail Conditional Expectation vs. Value-at-Risk#

Under MAR33.2, Expected Shortfall at confidence level $\alpha = 0.975$ is defined mathematically as the conditional expectation of portfolio profit-and-loss (P&L) distribution $L$ exceeding the $\alpha$-quantile threshold $VaR_{\alpha}(L)$:

$$ES{\alpha}(L) = \frac{1}{1 - \alpha} \int{\alpha}^{1} qu(L)\,du = \mathbb{E}\left[ L \,\middle|\, L \ge VaR{\alpha}(L) \right]$$

For a Gaussian reference distribution $\mathcal{N}(\mu, \sigma^2)$, the analytical ratio of the standard normal probability density function $\phi(\Phi^{-1}(0.975))$ to the tail probability $(1 - 0.975 = 0.025)$ yields a tail multiplier of $2.3378\sigma$, closely matching the $99\%$ normal VaR quantile $\Phi^{-1}(0.99) = 2.3263\sigma$. However, for leptokurtic trading book distributions exhibiting fat tails (Student-$t$ with $\nu \le 5$ degrees of freedom or jump-diffusion dynamics), $ES{97.5\%}$ produces capital charges 18% to 42% higher than $VaR{99\%}$, while satisfying the Artzner-Delbaen-Eber-Heath sub-additivity axiom ($ES(A + B) \le ES(A) + ES(B)$) required for coherent convex optimization.

2.2 The Five-Tier Liquidity Horizon Cascade (MAR33.4 – MAR33.12)#

Rather than computing separate multi-month historical simulations—which suffer from overlapping sample autocorrelation and sparse data degrees of freedom—BCBS MAR33.4 mandates that banks compute base Expected Shortfall shocks over a $LH1 = 10\text{-day}$ base horizon and scale nested subsets of risk factors $Q(RF, j)$ across five discrete supervisory liquidity horizons: $LHj \in \{10, 20, 40, 60, 120\}$ business days.

Let $ES{t}(P, j)$ denote the 10-day Expected Shortfall calculated when holding all risk factors with assigned liquidity horizon $LHk < LHj$ constant, shocking only the active risk factor subset $Q(RF, j) = \{RFk \mid LHk \ge LHj\}$. The marginal incremental liquidity-horizon contribution is scaled by the square-root-of-time differential and aggregated via a quadratic root-sum-of-squares cascade:

2.3 Reduced-Set Stressed Calibration & Cross-Asset Constraint ($\rho = 0.50$)#

Because full-revaluation simulation across all five liquidity subsets for every historical 12-month window since 2007 is computationally prohibitive, MAR33.5 permits an Indirect Stressed ES Approximation using a Reduced Set of Risk Factors ($R$) alongside the Full Set ($F$):

$$ES = ES{R,S} \times \max\left(1.0,\, \frac{ES{F,C}}{ES_{R,C}}\right)$$

Here $ES{R,S}$ is calibrated over the most severe 12-month Stressed Window ($S$) (typically 2008–2009 or March 2020), while $ES{F,C}$ and $ES{R,C}$ are calibrated over the Current 12-month period ($C$). By statute (MAR33.6), the reduced set $R$ must explain at least 75% of the variance of the full Expected Shortfall ($ES{R,C} / ES{F,C} \ge 0.75$). Furthermore, under MAR33.13, the Internals Model Capital Charge ($IMCC$) blends the unconstrained empirical portfolio charge $IMCC(C)$ with the linear sum of standalone asset-class charges $\sum{B=1}^{5} IMCC(B)$ using $\rho = 0.50$, capping cross-asset diversification at 50%.

3. Desk-Level Model Eligibility Gates: RFET, NMRF & P&L Attribution (PLAT)#

Under the Basel III Endgame framework (MAR30 – MAR32), internal models approval is granted, monitored, and revoked at the granular Trading Desk level [1]. Every trading desk must continuously pass two quantitative verification regimes to avoid mandatory fallback to the Standardized Approach (SA).

3.1 The Risk Factor Eligibility Test (RFET) & Non-Modellable Risk Factors (MAR31.12 – MAR33.21)#

A risk factor mapped to a desk's Risk-Theoretical P&L ($RTPL$) is classified as modellable only if it passes one of two quantitative verifiability criteria over the preceding 12-month period:

1. Criterion A (24-Observation Standard): At least 24 verifiable real prices per year, with no 90-day period containing fewer than 4 verifiable real prices (operationalized under EBA RTS [4] as no more than one 30-day gap).
2. Criterion B (100-Observation High-Frequency Standard): At least 100 verifiable real prices over the preceding 12-month period.

A verifiable real price must originate from an executed transaction or a committed, firm executable two-way quote from an arm's-length market maker; indicative consensus marks without trade tickets do not qualify. When a risk factor fails RFET, it is expelled from $IMCC$ and subjected to a standalone Stressed Expected Shortfall ($SES$) scenario calibrated over the most severe 12-month stress window (MAR33.17) at a liquidity horizon of $\max(LH_{\text{assigned}}, 20\text{ days})$:

$$SES{\text{Total}} = \sqrt{\sum{i=1}^{I} \left(ISES{NM,i}\right)^2} + \sqrt{\sum{j=1}^{J} \left(ISES{NM,j}\right)^2} + \sqrt{ \left( \rho{NM} \sum{k=1}^{K} SES{NM,k} \right)^2 + \left(1 - \rho{NM}^2\right) \sum{k=1}^{K} \left(SES_{NM,k}\right)^2 }$$

Under BCBS MAR33.19, the correlation parameter for general non-modellable risk factors is fixed at $\rho_{NM} = 0.60$, meaning 60% of all general NMRF charges add linearly. In dealer books, NMRFs regularly account for 30% to 55% of total IMA capital on exotic rates, inflation, and correlation desks.

3.2 The P&L Attribution Test (PLAT): Spearman Rank Correlation & Kolmogorov-Smirnov (MAR32.20 – MAR32.43)#

Even if 100% of a desk's risk factors pass RFET, the desk's risk engine ($RTPL$) must accurately reproduce the front-office pricing engine's Hypothetical P&L ($HPL$) across a rolling 250-trading-day sample (MAR32.24):

$$rs = \frac{\text{Cov}\big(R(HPL), R(RTPL)\big)}{\sigma{R(HPL)} \cdot \sigma{R(RTPL)}}, \qquad KS = \sup{x \in \mathbb{R}} \left| F{HPL}(x) - F{RTPL}(x) \right|$$

Under MAR32.39, trading desks are classified quarterly into three supervisory zones:

• Green Zone ($r_s > 0.80$ and $KS < 0.09$): Full IMA capital eligibility with zero capital surcharge.
• Amber Zone ($0.70 \le r_s \le 0.80$ or $0.09 \le KS \le 0.12$, and neither metric in Red): The desk retains IMA eligibility but incurs a mandatory Amber Zone Capital Surcharge proportional to the standardized capital gap: $\text{Surcharge}{\text{Amber}} = k \times \max(0, SA{\text{all}} - IMA{\text{all}})$, where $k = 0.50 \times \sum{i \in \text{Amber}} SAi / \sum{j \in \text{All IMA}} SA_j$.
• Red Zone ($r_s < 0.70$ or $KS > 0.12$): Immediate disqualification from IMA; the desk must compute 100% of its market risk capital under the FRTB Standardized Approach ($SA$) until it passes Green Zone thresholds and backtesting limits (MAR32.42).

4. ISDA SIMM v2.6 / v2.7 Initial Margin Calibration vs. FRTB Sensitivities-Based Method (SbM)#

While FRTB IMA governs regulatory capital for approved trading desks, virtually all banks (via the 72.5% Output Floor and fallback desks) and all bilateral OTC derivatives counterparties subject to BCBS-IOSCO Uncleared Margin Rules (UMR Phases 1–6) [8] must simultaneously compute variance-covariance sensitivity aggregations under the FRTB Sensitivities-Based Method (SbM) (MAR21) and ISDA SIMM [6].

4.1 Structural Isomorphism and Parametric Divergence: FRTB-SbM vs. ISDA SIMM#

Both FRTB-SbM and ISDA SIMM decompose portfolio risk into Delta ($\Delta$), Vega ($\mathcal{V}$), and Curvature ($CVR$) across six asset classes. Within each risk bucket $b$, weighted sensitivities $WSk = RWk \cdot sk \cdot CRk$ are aggregated via an intra-bucket correlation matrix $\rho{kl}$ and combined across buckets using an inter-bucket correlation matrix $\gamma{bc}$:

$$Kb = \sqrt{\max\left(0,\, \sum{k} WSk^2 + \sum{k} \sum{l \neq k} \rho{kl} WSk WSl\right)}, \qquad \text{Capital}{\text{Class}} = \sqrt{\sum{b} Kb^2 + \sum{b} \sum{c \neq b} \gamma{bc} Sb Sc} + K_{\text{residual}}$$

Despite sharing this algebraic backbone, quantitative engineers must account for four fundamental calibration divergences between ISDA SIMM v2.6/v2.7 and FRTB-SbM:

1. 99% 10-Day VaR (SIMM) vs. 97.5% Variable-Horizon Stressed ES (FRTB-SbM): ISDA SIMM is calibrated to a 99% Value-at-Risk ($VaR_{99\%}$) over a uniform 10-day Margin Period of Risk ($MPOR$) using a 3+1 year historical lookback (3 recent rolling years plus 1 stress year). Conversely, FRTB-SbM risk weights (MAR21.39 – MAR21.95) replicate 97.5% Stressed Expected Shortfall across variable liquidity horizons (10d to 120d), making FRTB-SbM credit and equity risk weights 1.4x to 2.8x larger than ISDA SIMM weights.
2. Product Class Silos (SIMM) vs. Portfolio-Wide Risk Class Netting (FRTB-SbM): Under BCBS-IOSCO UMR rules [8], ISDA SIMM partitions every trade into one of four mutually exclusive Product Silos: (1) Interest Rates & FX, (2) Credit, (3) Equity, and (4) Commodity. Total SIMM initial margin is the strict linear sum across silos ($IM{\text{SIMM}} = IM{\text{RatesFX}} + IM{\text{Credit}} + IM{\text{Equity}} + IM_{\text{Commodity}}$). Thus, an interest-rate hedge in the RatesFX silo cannot offset interest-rate delta embedded in an equity total return swap (Equity silo) under ISDA SIMM, whereas FRTB-SbM nets all interest rate sensitivities portfolio-wide.
3. Concentration Thresholds ($T_b$): ISDA SIMM applies explicit dollar Concentration Thresholds ($Tb$) via $CRb = \max\left(1.0, \sqrt{|\sum{k \in b} sk| / Tb}\right)$, scaling both effective risk weights and cross-bucket correlation penalties when a counterparty's net DV01 or vega exceeds $Tb$.
4. Three-Scenario Correlation Stress (FRTB-SbM Only): FRTB-SbM computes capital across Medium (Base), High ($1.25 \times \rho_{kl}$, capped at $1.00$), and Low ($\max(2 \times \rho{kl} - 1,\, 0.75 \times \rho{kl})$) correlation matrices (MAR21.6), taking the maximum charge across the three scenarios.

5. Counterparty Credit Risk (SA-CCR CRE52), CVA (MAR50), and the Cross-Margin Optimization Frontier#

A trading desk's return on equity ($RoE$) is governed by the coupled interaction of FRTB Market Risk RWA, SA-CCR Counterparty Credit Risk RWA, Credit Valuation Adjustment (CVA) RWA, and the funding cost of posting initial margin (MVA).

5.1 SA-CCR Exposure at Default (CRE52) and the $\alpha = 1.4$ Multiplier#

Under CRE52.1 [2], Exposure at Default ($EAD$) for derivatives netting sets is defined as:

$$EAD{\text{SA-CCR}} = \alpha \times (RC + PFE), \quad \alpha = 1.40, \quad PFE = m \times \text{AddOn}{\text{agg}}$$

For margined netting sets, Replacement Cost is $RC = \max(V - C,\, TH + MTA - NICA,\, 0)$, and the PFE multiplier $m = \min\left(1, 0.05 + 0.95 \exp\left(\frac{V - C}{1.90 \times \text{AddOn}{\text{agg}}}\right)\right)$ dampens exposure when initial margin over-collateralizes the netting set ($V - C < 0$). Because segregated bilateral initial margin posted under UMR cannot reduce $NICA$ for the posting party (as it is held in a bankruptcy-remote third-party custodian account under CFTC Part 23 [7]), posting $\$1\text{B}$ of ISDA SIMM initial margin suppresses the counterparty's $PFE$ multiplier toward the $5\%$ floor for the receiver, yet imposes a permanent funding drag ($MVA = \int0^T sF(t) \mathbb{E}^{\mathbb{Q}}[D(0,t) IM{\text{SIMM}}(t)] dt$) on the poster.

5.2 Cleared CCP vs. Bilateral UMR vs. Multilateral Compression Trade-Offs#

Quantitative XVA and capital optimization desks continuously re-balance exposures across three structural venues:

1. Central Counterparty (CCP) Clearing (LCH SwapClear, CME, Eurex): Carries a preferential 2% Qualifying CCP ($QCCP$) risk weight (CRE54.14) and a 5-day MPOR ($MPOR_{\text{CCP}} = 5\text{d}$ vs. $10\text{d}$ bilateral), lowering initial margin by roughly $1 - \sqrt{5/10} \approx 29.3\%$. However, CCP initial margin must be funded in cash or sovereign collateral, and directional books lose cross-asset netting against bespoke OTC structures remaining in bilateral CSA silos.
2. Bilateral Uncleared SIMM Netting Sets: Subject to $10\text{-day}$ MPOR and counterparty credit risk weights ranging from $20\%$ to $100\%$ under Standardized Credit Risk (CRE20), plus BA-CVA or SA-CVA capital (MAR50).
3. Multilateral Risk Compression & SIMM Rebalancing Cycles (OSTTRA TriOptima / Quantile): By injecting zero-NPV market-neutral basis swaps, FX forwards, or swaption straddles between pairs of dealers, compression engines transfer delta and vega sensitivities from counterparty pairs where SIMM concentration thresholds ($T_b$) are breached to pairs with offsetting exposures—routinely reducing aggregate bilateral SIMM IM by 25% to 45% and SA-CCR $\text{AddOn}_{\text{agg}}$ by 30% to 50% without altering front-office economic positioning.

6. Jurisdictional Divergence Matrix: US Basel III Endgame vs. EU CRR III vs. UK PRA PS9/24 vs. APAC (2025–2026)#

A critical operational challenge for global banking groups in 2026 is the fragmented timeline and supervisory calibration across the four major regulatory blocs.

6.1 United States (12 CFR Part 217), European Union (CRR III Art. 461a), UK (PS9/24) & APAC#

Following extensive industry feedback on the July 2023 Notice of Proposed Rulemaking (NPR) [3], the Federal Reserve Board outlined structural revisions leading into the 2026 final rulemaking cycle: Category III/IV regional banks ($\$$100B–$\$$700B assets) are largely exempted from internal model overhauls, while US G-SIBs face a recalibrated FRTB implementation targeting an aggregate CET1 increase of roughly 9% (down from 19% in the 2023 NPR), alongside targeted SA-CCR client-clearing relief and broader vendor data pool recognition for RFET.

In the European Union, while CRR III (Regulation (EU) 2024/1623) entered into force on January 1, 2025 for credit risk, SA-CCR, CVA, and the 50% Output Floor phase-in (reaching 72.5% by 2030), the European Commission invoked Article 461a of the CRR [4] to postpone binding Pillar 1 own-funds FRTB application until 2026–2027 to preserve transatlantic parity, while maintaining mandatory quarterly COREP FRTB-SA/IMA reporting supervised by the EBA. Similarly, the UK Prudential Regulation Authority (PRA) published PS9/24 [5] (detailed in our UK FCA & PRA Profile), aligning Basel 3.1 go-live to January 1, 2027 with bespoke Default Risk Charge ($DRC$) sovereign adjustments. Conversely, FINMA in Switzerland (ERV), the Monetary Authority of Singapore (MAS) (MAS Notice 637), and the HKMA brought final FRTB Pillar 1 rules into force across 2024–2025.

7. Production Risk Engine Implementation: Adjoint Algorithmic Differentiation (AAD) & Capital Allocation#

7.1 Adjoint Algorithmic Differentiation (AAD) for Real-Time SIMM & PLAT#

Under FRTB PLAT (MAR32.20), any discrepancy between the front-office pricing library ($HPL$) and the risk engine's Taylor-expansion or grid-interpolation approximation ($RTPL$) drives the Kolmogorov-Smirnov ($KS$) metric above the 0.09 Green Zone ceiling or the 0.12 Red Zone cliff. Simultaneously, ISDA SIMM requires exact first-order Delta ($\partial V / \partial ri$), Vega ($\partial V / \partial \sigmak$), and second-order cross-gamma sensitivities across thousands of vertices within intraday pre-trade check latencies ($<50\text{ ms}$).

Replacing finite-difference ("bump-and-revalue") Greek calculations—which scale linearly $\mathcal{O}(N)$ with risk factor count $N$—with reverse-mode Adjoint Algorithmic Differentiation (AAD) computes the complete gradient vector $\nabla V = (\partial V / \partial x1, \dots, \partial V / \partial xN)$ in a single backward pass bounded by $\le 4 \times$ a single forward valuation ($\mathcal{O}(1)$ complexity relative to $N$). Embedding AAD directly into front-office C++ / CUDA Monte Carlo pricing kernels enables unified front-to-back valuation ($HPL \equiv RTPL$, securing $rs > 0.94$ and $KS < 0.04$) and real-time pre-trade $\Delta IM{\text{SIMM}}$, $\Delta EAD_{\text{SA-CCR}}$, and $\Delta KVA$ routing.

7.2 Total IMA Desk Capital Aggregation & Euler Allocation (MAR33.41 – MAR33.43)#

For trading desks maintaining Green or Amber PLAT status and passing backtesting (MAR32.5), aggregate Internal Models Approach capital ($C_{\text{IMA}}$) is assembled via MAR33.43:

$$C{\text{IMA}} = \max\left(IMCC{t-1} + SES{t-1},\, mc \cdot \overline{IMCC}{60d} + \overline{SES}{60d}\right) + DRC + \text{Surcharge}_{\text{Amber}}$$

where $mc \ge 1.50$ is the supervisory multiplier, $\overline{IMCC}{60d}$ and $\overline{SES}{60d}$ are 60-day moving averages, and $DRC$ is the Default Risk Charge (MAR33.18) computed via a two-factor Gaussian copula Monte Carlo simulation at 99.9% 1-year confidence with a 0.03% (3 bps) PD floor. Because $ES{97.5\%}$ is homogeneous of degree one, desks allocate $IMCC$ down to individual trades using Euler's Homogeneous Function Theorem: $\text{Euler } ESi = \mathbb{E}[Li \mid LP \ge VaR{97.5\%}(L_P)]$. For execution teams comparing prime brokerage margin models against dealer balance sheet costs, consult our Institutional Broker & Clearing Comparison Hub and Global Regulatory Directory.

Primary Regulatory Filings, Statutory Citations & Empirical References#

• [1] Basel Committee on Banking Supervision (BCBS), Minimum Capital Requirements for Market Risk (Consolidated Basel Framework MAR10–MAR50), Bank for International Settlements (BIS): https://www.bis.org/basel_framework/standard/MAR.htm
• [2] Basel Committee on Banking Supervision (BCBS), Standardised Approach for Counterparty Credit Risk (SA-CCR, CRE52), Bank for International Settlements (BIS): https://www.bis.org/basel_framework/standard/CRE.htm
• [3] Board of Governors of the Federal Reserve System, FDIC & OCC, Regulatory Capital Rule: Large Banking Organizations and Banking Organizations with Significant Trading Activity (Basel III Endgame): https://www.federalreserve.gov/supervisionreg/basel-endgame.htm
• [4] European Banking Authority (EBA), Regulatory Technical Standards (RTS) on FRTB Risk Factor Eligibility Test (RFET), P&L Attribution (PLAT), and CRR III Market Risk Implementation: https://www.eba.europa.eu/regulation-and-policy/market-risk
• [5] Bank of England Prudential Regulation Authority (PRA), PS9/24 — Implementation of the Basel 3.1 Standards Near-Final Policy Statement (Market Risk & Output Floor): https://www.bankofengland.co.uk/prudential-regulation/publication/2024/september/implementation-of-the-basel-3-1-standards-near-final-policy-statement-part-2
• [6] International Swaps and Derivatives Association (ISDA), ISDA SIMM Methodology v2.6 / v2.7 & Uncleared Margin Reform Governance Framework: https://www.isda.org/margin-reform/
• [7] Commodity Futures Trading Commission (CFTC), 17 CFR Part 23 — Margin Requirements for Uncleared Swaps for Swap Dealers and Major Swap Participants: https://www.cftc.gov/LawRegulation/FederalRegister/index.htm
• [8] Basel Committee on Banking Supervision & IOSCO, Margin Requirements for Non-Centrally Cleared Derivatives (Consolidated Framework): https://www.bis.org/bcbs/publ/d499.htm