# How Can Firms Standardize Risk Factors across Different Margin Models? ▴ Question

**Published:** 2025-07-30
**Categories:** Question

---

![A sleek, dark, angled component, representing an RFQ protocol engine, rests on a beige Prime RFQ base. Flanked by a deep blue sphere representing aggregated liquidity and a light green sphere for multi-dealer platform access, it illustrates high-fidelity execution within digital asset derivatives market microstructure, optimizing price discovery](https://options.greeks.live/wp-content/uploads/2025/07/advanced-rfq-system-for-digital-asset-derivatives-price-discovery.jpg)

![A deconstructed spherical object, segmented into distinct horizontal layers, slightly offset, symbolizing the granular components of an institutional digital asset derivatives platform. Each layer represents a liquidity pool or RFQ protocol, showcasing modular execution pathways and dynamic price discovery within a Prime RFQ architecture for high-fidelity execution and systemic risk mitigation](https://options.greeks.live/wp-content/uploads/2025/08/modular-institutional-digital-asset-derivatives-execution-framework-dissected-for-optimal-rfq-performance.jpg)

## Concept

The imperative to standardize risk factors across disparate [margin models](https://prime.greeks.live/area/margin-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is a direct function of institutional survival in a fragmented market architecture. An institution’s portfolio is subject to a multitude of risk assessments, each conducted through the unique prism of a specific counterparty or clearinghouse model. A central clearing counterparty (CCP) employing a SPAN-based methodology views risk through a grid of predetermined price and volatility shifts. A prime broker offering [portfolio margining](https://prime.greeks.live/area/portfolio-margining/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) uses a Value-at-Risk (VaR) model, deriving risk from historical or simulated market scenarios.

Simultaneously, bilateral derivative trades fall under the ISDA Standard Initial [Margin Model](https://prime.greeks.live/area/margin-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (SIMM), which operates on a granular, sensitivity-based calculation. The result is a fractured, operationally burdensome, and strategically opaque risk landscape.

A firm that fails to impose its own coherent risk language upon this chaos is perpetually reactive. It is forced to manage its capital and collateral on a per-model basis, leading to trapped liquidity and an inability to view its own portfolio risk in a holistic, aggregated manner. The core of the standardization challenge is the translation of idiosyncratic model inputs into a unified, internal framework. This process is not one of finding a simple lowest common denominator.

It is an act of architectural design, creating a canonical risk data model that serves as the firm’s single source of truth. From this central model, the firm can then generate the specific risk representations required by each external entity. This internal system provides a stable, consistent view of risk, allowing for strategic capital allocation and precise hedging actions that are independent of any single counterparty’s idiosyncratic model.

> A standardized internal risk framework allows a firm to translate the chaotic language of multiple external margin models into a single, coherent internal dialogue for strategic decision-making.

This undertaking moves a firm from a defensive posture of meeting disparate margin calls to an offensive one of optimizing its portfolio for [capital efficiency](https://prime.greeks.live/area/capital-efficiency/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The ability to accurately forecast margin requirements across all venues from a single, unified position file becomes a significant competitive advantage. It allows for pre-trade analysis that accounts for the marginal risk contribution of a new position not just to one model, but to all relevant models simultaneously.

This systemic view transforms [risk management](https://prime.greeks.live/area/risk-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) from a compliance function into a core component of the firm’s performance engine. The standardization of [risk factors](https://prime.greeks.live/area/risk-factors/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is the foundational engineering required to build a truly integrated and efficient trading enterprise.

The historical evolution from simpler models like SPAN to more complex VaR and sensitivity-based frameworks has amplified this need. The original SPAN methodology determined risk across a limited set of 16 simplified market scenarios. In contrast, modern VaR-based models used by major CCPs, such as CME’s SPAN 2 or ICE’s IRM 2, can incorporate thousands of inputs to generate a more responsive and accurate margin requirement.

This leap in complexity, while offering more precise risk measurement, creates a significant operational challenge. Without a standardization layer, a firm’s risk and operations teams are left to manually reconcile and manage these highly varied data demands, an untenable position in volatile markets where speed and accuracy are paramount.

![A sleek, split capsule object reveals an internal glowing teal light connecting its two halves, symbolizing a secure, high-fidelity RFQ protocol facilitating atomic settlement for institutional digital asset derivatives. This represents the precise execution of multi-leg spread strategies within a principal's operational framework, ensuring optimal liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-system-2.jpg)

![Visualizing institutional digital asset derivatives market microstructure. A central RFQ protocol engine facilitates high-fidelity execution across diverse liquidity pools, enabling precise price discovery for multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-across-multi-asset-liquidity.jpg)

## Strategy

Developing a strategy for standardizing risk factors requires a firm to define its objectives with precision. The primary goals are typically enhanced capital efficiency, superior risk visibility, and increased operational resilience. The strategic pathway involves a fundamental choice ▴ adopt an industry standard like [ISDA SIMM](https://prime.greeks.live/area/isda-simm/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) as the internal benchmark, or construct a proprietary canonical risk framework that maps to all external models.

The former offers expediency and alignment with a significant portion of the [non-cleared derivatives](https://prime.greeks.live/area/non-cleared-derivatives/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) market. The latter provides maximum flexibility and a more holistic view that encompasses both bilateral and centrally cleared exposures.

![A precision-engineered institutional digital asset derivatives execution system cutaway. The teal Prime RFQ casing reveals intricate market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-protocol-core-engine.jpg)

## Choosing the Architectural Approach

The decision to build a custom framework versus adopting an existing standard depends on the firm’s trading profile and operational capacity. A firm primarily engaged in non-cleared derivatives might find adopting ISDA SIMM as its internal standard to be a highly efficient path. SIMM is already a well-defined, sensitivity-based methodology with prescribed risk factors, weights, and correlations across four product classes ▴ RatesFX, Credit, Equity, and Commodity. This provides a ready-made structure for internal risk representation.

A firm with significant activity across both centrally cleared and bilateral markets may find a proprietary approach more suitable. Such a strategy involves creating a firm-specific taxonomy of risk factors that is granular enough to capture the nuances of all relevant models. This internal “risk language” becomes the pivot point for all risk calculations. For instance, a single interest rate swap position would be decomposed into sensitivities against a standardized internal yield curve.

This internal representation would then be transformed, via a mapping layer, into the specific formats required by a CCP’s [VaR model](https://prime.greeks.live/area/var-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), another CCP’s SPAN model, and a bilateral counterparty’s SIMM calculation. This approach centralizes the complexity, requiring a significant initial investment in technology and quantitative talent but yielding a powerful, unified view of portfolio risk.

> The strategic decision hinges on whether to adopt a market-standard language like ISDA SIMM or to build a proprietary, universal translator for all forms of risk communication.

![A futuristic, intricate central mechanism with luminous blue accents represents a Prime RFQ for Digital Asset Derivatives Price Discovery. Four sleek, curved panels extending outwards signify diverse Liquidity Pools and RFQ channels for Block Trade High-Fidelity Execution, minimizing Slippage and Latency in Market Microstructure operations](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-platform-2.jpg)

## How Do Different Models Define Risk?

Understanding the fundamental differences in how margin models conceptualize risk is critical to forming a coherent strategy. Each model represents a different philosophy of risk measurement, and a standardization framework must be ableto bridge these philosophical divides. The table below compares the [risk factor](https://prime.greeks.live/area/risk-factor/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) granularity and methodology of three dominant model types.

| Model Type | Primary Methodology | Risk Factor Granularity | Key Characteristics |
| --- | --- | --- | --- |
| SPAN (Standard Portfolio Analysis of Risk) | Scenario-Based Grid | Low. Risk is defined by scanning ranges (price shifts) and volatility shifts for predefined product groups. | Relies on a set of 16 standardized scenarios. Inter-product offsets are applied via explicit correlation parameters. It is deterministic and relatively transparent. |
| VaR (Value-at-Risk) | Historical or Monte Carlo Simulation | High. Risk is derived from the behavior of thousands of underlying risk factors (e.g. individual interest rates, equity prices, volatilities) over a look-back period. | Captures portfolio diversification and non-linear risks more effectively. Correlations are implicitly captured in the simulation. Less transparent due to the complexity of the underlying scenarios. |
| ISDA SIMM (Standard Initial Margin Model) | Sensitivity-Based | Very High. Risk is defined by sensitivities (Delta, Vega, Curvature) to a prescribed set of granular risk factors within specific asset classes (e.g. interest rate tenor buckets, credit quality buckets). | Designed for non-cleared derivatives to create a common, verifiable standard. Utilizes ISDA-provided risk weights and correlations for aggregation. Reduces disputes between counterparties. |

![Internal hard drive mechanics, with a read/write head poised over a data platter, symbolize the precise, low-latency execution and high-fidelity data access vital for institutional digital asset derivatives. This embodies a Principal OS architecture supporting robust RFQ protocols, enabling atomic settlement and optimized liquidity aggregation within complex market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-precision-execution-system-microstructure.jpg)

## The Strategic Value of a Unified Risk Engine

A successful standardization strategy culminates in the creation of a unified risk engine. This engine serves as the central nervous system for risk management and capital allocation. Its benefits extend beyond operational efficiency.

- **Pre-Trade Margin Analysis** ▴ Traders can query the engine to understand the holistic margin impact of a potential trade across all relevant CCPs and bilateral counterparties before execution. This allows for smarter trade routing and structuring to minimize capital consumption. 

- **Holistic Risk Aggregation** ▴ Management can view aggregated risk exposures based on a consistent internal methodology, rather than trying to reconcile disparate figures from multiple models. This enables a true understanding of the firm’s net risk position. 

- **Enhanced Stress Testing** ▴ The standardized risk factors provide a stable basis for firm-wide stress testing. Scenarios can be applied to the canonical risk representation, and the impact can be propagated out to predict margin calls from all sources under various market conditions. 

- **Operational Scalability** ▴ When a new CCP model is introduced or an existing one is updated, the firm only needs to update the mapping layer for that specific model. The core risk engine and internal representation remain unchanged, providing significant operational leverage. 

Ultimately, the strategy is about building an architectural advantage. By investing in a system that standardizes risk representation, a firm gains a level of control and insight that is impossible to achieve when operating as a passive recipient of external margin calculations. It is the difference between navigating a storm with a single, coherent map and trying to piece together conflicting charts from different cartographers while the waves are crashing.

![A sleek, futuristic object with a glowing line and intricate metallic core, symbolizing a Prime RFQ for institutional digital asset derivatives. It represents a sophisticated RFQ protocol engine enabling high-fidelity execution, liquidity aggregation, atomic settlement, and capital efficiency for multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/advanced-institutional-digital-asset-derivatives-rfq-protocol-execution-engine-for-optimal-liquidity-aggregation-and-atomic-settlement.jpg)

![Stacked, modular components represent a sophisticated Prime RFQ for institutional digital asset derivatives. Each layer signifies distinct liquidity pools or execution venues, with transparent covers revealing intricate market microstructure and algorithmic trading logic, facilitating high-fidelity execution and price discovery within a private quotation environment](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-execution-framework.jpg)

## Execution

Executing a strategy to standardize risk factors is a complex, multi-stage project that requires a fusion of quantitative analysis, technology engineering, and operational process design. The objective is to build a robust and scalable system that can consume portfolio data, calculate risk sensitivities against a canonical model, and then translate those risks into the specific requirements of each external margin model. This section provides an operational playbook for this process.

![Smooth, glossy, multi-colored discs stack irregularly, topped by a dome. This embodies institutional digital asset derivatives market microstructure, with RFQ protocols facilitating aggregated inquiry for multi-leg spread execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-for-prime-os-best-execution.jpg)

## The Operational Playbook for Standardization

The implementation can be broken down into a series of distinct, sequential phases. Each phase builds upon the last, culminating in a fully functional and validated risk standardization engine.

- **Phase 1 Inventory and Taxonomy Creation** ▴ The initial step is to conduct a comprehensive inventory of every risk factor across every margin model the firm is subject to. This includes the tenor buckets for interest rates in ISDA SIMM, the commodity codes in CME SPAN 2, the equity indices in a CCP’s VaR model, and so on. The output of this phase is a master risk factor taxonomy that organizes these disparate inputs into a logical, hierarchical structure. For example ▴ Commodity -> Energy -> Crude Oil -> WTI -> Front Month Future. 

- **Phase 2 Design of the [Canonical Risk Model](https://prime.greeks.live/area/canonical-risk-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** ▴ With the taxonomy established, the firm must design its internal, canonical risk model. This involves defining the standardized risk factors the firm will use as its internal benchmark. A key decision here is the level of granularity. The canonical model must be granular enough to accurately map to the most detailed external model (often ISDA SIMM). This model becomes the firm’s “Rosetta Stone” for risk. 

- **Phase 3 Data Ingestion and Sensitivity Calculation** ▴ This phase involves building the technological infrastructure. A data pipeline must be created to ingest all trade and position data from the firm’s books and records systems. A sensitivity engine is then built to calculate the portfolio’s exposure to each of the canonical risk factors defined in Phase 2. This engine will compute the necessary Greeks (Delta, Vega, Curvature) for all positions against the standardized factors. 

- **Phase 4 Development of the Mapping and Transformation Layer** ▴ This is the core of the execution. This software layer contains the logic to translate the firm’s internal risk sensitivities into the specific formats required by each external model. For a VaR model, this might involve using the sensitivities to shock historical data. For ISDA SIMM, it involves populating the standard CRIF (Common Risk Interchange Format) file. For SPAN, it involves mapping the risk to the appropriate scanning ranges. 

- **Phase 5 Reconciliation and Backtesting** ▴ Once the engine is built, it must be rigorously validated. A daily reconciliation process must be established to compare the margin numbers generated by the internal engine with the actual margin calls received from CCPs and counterparties. Any discrepancies must be investigated and used to refine the mapping logic. Backtesting the model against historical data is also crucial to ensure it performs as expected under stressed market conditions. 

![Segmented beige and blue spheres, connected by a central shaft, expose intricate internal mechanisms. This represents institutional RFQ protocol dynamics, emphasizing price discovery, high-fidelity execution, and capital efficiency within digital asset derivatives market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-optimal-execution.jpg)

## What Is the Core Transformation Logic?

The transformation layer is where the theoretical strategy becomes a practical reality. It contains the rules-based and model-based logic for mapping. For instance, a firm’s canonical model might have 50 granular interest rate tenor points for the USD curve. ISDA SIMM requires a specific set of 12 tenor buckets.

The transformation logic would include an interpolation algorithm to distribute the risk from the 50 internal points to the 12 SIMM buckets. The table below illustrates a simplified mapping for a hypothetical portfolio’s risk factors.

| Canonical Risk Factor (Internal Model) | Portfolio Sensitivity (Internal) | Target Model | Transformation Logic | Resulting Model Input |
| --- | --- | --- | --- | --- |
| USD IR DV01 (10Y) | $15,000 | ISDA SIMM | Map to the ’10y’ tenor bucket in the Interest Rate risk class. | Risk_Factor=USD.Rate.10Y, Amount=15000, Type=Delta |
| AAPL Equity Delta | $500,000 | CCP VaR Model | Identify AAPL as a US-listed large-cap tech stock. Use the sensitivity to shock the AAPL price in the historical scenario set. | Contributes to the portfolio’s overall P/L distribution in the VaR calculation. |
| EUR/USD Vega | $5,000 per vol point | ISDA SIMM | Map to the ‘EUR/USD’ vega factor in the RatesFX risk class. | Risk_Factor=EUR.USD.Vega, Amount=5000, Type=Vega |
| WTI Crude Oil Future (Front Month) | 1,000 bbl | CME SPAN 2 | Identify the contract as belonging to the ‘Crude Oil’ combined commodity. Map the position to the appropriate tier and scanning range. | Position data fed into the SPAN algorithm to calculate scanning risk. |

![An abstract composition of interlocking, precisely engineered metallic plates represents a sophisticated institutional trading infrastructure. Visible perforations within a central block symbolize optimized data conduits for high-fidelity execution and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-multi-leg-execution-layer.jpg)

## System Integration and Technological Architecture

The execution of this strategy requires a well-defined technological architecture. The system is typically composed of several integrated modules:

- **A Centralized Trade Store** ▴ A database that consolidates position data from all trading systems across the firm. 

- **A Market Data Hub** ▴ A service that captures and normalizes real-time and historical market data required for the sensitivity and margin calculations. 

- **A Quantitative Library** ▴ A core library of pricing models and sensitivity calculation functions. This is the “quant engine” that computes the risks against the canonical model. 

- **The Mapping Engine** ▴ The software module that implements the transformation logic described above. This is often a rules-based engine that can be easily updated as external model methodologies change. 

- **A Reporting and Analytics Dashboard** ▴ A user interface that allows risk managers and traders to view aggregated risk, drill down into specific positions, run what-if scenarios, and analyze reconciliation breaks. 

This architecture ensures that the process is automated, scalable, and auditable. The investment in this infrastructure is substantial, but it provides the foundation for a modern, efficient, and strategically agile risk management function. It moves the firm from being a mere price-taker in the market for risk capital to an intelligent architect of its own risk profile.

![A central processing core with intersecting, transparent structures revealing intricate internal components and blue data flows. This symbolizes an institutional digital asset derivatives platform's Prime RFQ, orchestrating high-fidelity execution, managing aggregated RFQ inquiries, and ensuring atomic settlement within dynamic market microstructure, optimizing capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-trading-system-facilitating-high-fidelity-rfq-protocol-optimization-for-best-execution-and-atomic-settlement.jpg)

## References

- Harris, Larry. “Trading and Exchanges ▴ Market Microstructure for Practitioners.” Oxford University Press, 2003.

- International Swaps and Derivatives Association. “ISDA SIMM Methodology.” Version 2.3, 8 July 2020.

- CME Group. “CME SPAN Standard Portfolio Analysis of Risk.” 2019.

- Barone-Adesi, Giovanni, et al. “VaR Without Correlations for Portfolios of Derivative Securities.” Risk Magazine, 1999.

- Committee on Payment and Settlement Systems & International Organization of Securities Commissions. “Principles for financial market infrastructures.” Bank for International Settlements, April 2012.

- Murphy, David, et al. “Procyclicality of central counterparty margin models ▴ systemic problems need systemic approaches.” Journal of Financial Market Infrastructures, vol. 9, no. 4, 2022.

- Cont, Rama. “Custom v. Standardized Risk Models.” arXiv, 2014.

- Nasdaq. “New Portfolio Margin Models Bring Benefits, but Also Challenges, to Risk Management.” Nasdaq, 2023.

![A split spherical mechanism reveals intricate internal components. This symbolizes an Institutional Digital Asset Derivatives Prime RFQ, enabling high-fidelity RFQ protocol execution, optimal price discovery, and atomic settlement for block trades and multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-revealing-rfq-execution-core-mechanics.jpg)

## Reflection

The framework presented here provides a blueprint for systemic control over risk and capital. The transition from a reactive to a proactive stance on margin management is not merely an operational upgrade; it is a fundamental shift in institutional posture. As you consider your own firm’s architecture, the central question emerges ▴ is your risk management system a collection of disparate responses to external demands, or is it a coherent, centralized intelligence engine that drives strategic advantage?

The capacity to define, measure, and manage risk according to a single, unified internal logic is the defining characteristic of a market-leading institution in the modern financial landscape. The ultimate value of this standardization lies in the clarity it provides, enabling decisive action in a complex world.

![A central blue sphere, representing a Liquidity Pool, balances on a white dome, the Prime RFQ. Perpendicular beige and teal arms, embodying RFQ protocols and Multi-Leg Spread strategies, extend to four peripheral blue elements](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-rfq-protocol-execution-framework.jpg)

## Glossary

<a href="https://prime.greeks.live/area/portfolio-margining/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A modular, institutional-grade device with a central data aggregation interface and metallic spigot. This Prime RFQ represents a robust RFQ protocol engine, enabling high-fidelity execution for institutional digital asset derivatives, optimizing capital efficiency and best execution](https://options.greeks.live/wp-content/uploads/2025/08/principals-institutional-digital-asset-derivatives-prime-os-for-optimized-rfq-protocol-best-execution-system.jpg)

## Portfolio Margining

Meaning ▴ Portfolio Margining is an advanced, risk-based margining system that precisely calculates margin requirements for an entire portfolio of correlated financial instruments, rather than assessing each position in isolation. </a><a href="https://prime.greeks.live/area/margin-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An angular, teal-tinted glass component precisely integrates into a metallic frame, signifying the Prime RFQ intelligence layer. This visualizes high-fidelity execution and price discovery for institutional digital asset derivatives, enabling volatility surface analysis and multi-leg spread optimization via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-for-digital-asset-derivatives-execution.jpg)

## Margin Models

Meaning ▴ Margin Models are sophisticated quantitative frameworks employed in crypto derivatives markets to determine the collateral required for leveraged trading positions, ensuring financial stability and mitigating systemic risk. </a><a href="https://prime.greeks.live/area/margin-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A dual-toned cylindrical component features a central transparent aperture revealing intricate metallic wiring. This signifies a core RFQ processing unit for Digital Asset Derivatives, enabling rapid Price Discovery and High-Fidelity Execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-core-best-execution-prime-os.jpg)

## Margin Model

Meaning ▴ A Margin Model, within the architecture of crypto trading and lending platforms, is a sophisticated algorithmic framework designed to compute and enforce the collateral requirements, known as margin, for leveraged positions in digital assets. </a><a href="https://prime.greeks.live/area/capital-efficiency/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Brushed metallic and colored modular components represent an institutional-grade Prime RFQ facilitating RFQ protocols for digital asset derivatives. The precise engineering signifies high-fidelity execution, atomic settlement, and capital efficiency within a sophisticated market microstructure for multi-leg spread trading](https://options.greeks.live/wp-content/uploads/2025/08/institutional-grade-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-system.jpg)

## Capital Efficiency

Meaning ▴ Capital efficiency, in the context of crypto investing and institutional options trading, refers to the optimization of financial resources to maximize returns or achieve desired trading outcomes with the minimum amount of capital deployed. </a><a href="https://prime.greeks.live/area/risk-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract depiction of an advanced institutional trading system, featuring a prominent sensor for real-time price discovery and an intelligence layer. Visible circuitry signifies algorithmic trading capabilities, low-latency execution, and robust FIX protocol integration for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-high-fidelity-rfq-protocols.jpg)

## Risk Management

Meaning ▴ Risk Management, within the cryptocurrency trading domain, encompasses the comprehensive process of identifying, assessing, monitoring, and mitigating the multifaceted financial, operational, and technological exposures inherent in digital asset markets. </a><a href="https://prime.greeks.live/area/risk-factors/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision-engineered, multi-layered system component, symbolizing the intricate market microstructure of institutional digital asset derivatives. Two distinct probes represent RFQ protocols for price discovery and high-fidelity execution, integrating latent liquidity and pre-trade analytics within a robust Prime RFQ framework, ensuring best execution](https://options.greeks.live/wp-content/uploads/2025/08/digital-asset-derivatives-rfq-protocols-high-fidelity-execution.jpg)

## Risk Factors

Meaning ▴ Risk Factors, within the domain of crypto investing and the architecture of digital asset systems, denote the inherent or external elements that introduce uncertainty and the potential for adverse outcomes. </a><a href="https://prime.greeks.live/area/isda-simm/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A centralized RFQ engine drives multi-venue execution for digital asset derivatives. Radial segments delineate diverse liquidity pools and market microstructure, optimizing price discovery and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-institutional-digital-asset-derivatives.jpg)

## Isda Simm

Meaning ▴ ISDA SIMM, or the Standard Initial Margin Model, is a globally standardized methodology meticulously developed by the International Swaps and Derivatives Association for calculating initial margin requirements for non-cleared derivatives transactions. </a><a href="https://prime.greeks.live/area/non-cleared-derivatives/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, multi-layered digital asset derivatives platform highlights a teal sphere, symbolizing a core liquidity pool or atomic settlement node. The perforated white interface represents an RFQ protocol's aggregated inquiry points for multi-leg spread execution, reflecting precise market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-core-execution-system.jpg)

## Non-Cleared Derivatives

Meaning ▴ Non-Cleared Derivatives are financial contracts, such as options or swaps, whose settlement and risk management occur directly between two counterparties without the intermediation of a central clearing counterparty (CCP). </a><a href="https://prime.greeks.live/area/var-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A reflective digital asset pipeline bisects a dynamic gradient, symbolizing high-fidelity RFQ execution across fragmented market microstructure. Concentric rings denote the Prime RFQ centralizing liquidity aggregation for institutional digital asset derivatives, ensuring atomic settlement and managing counterparty risk](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-institutional-digital-asset-derivatives-2.jpg)

## Var Model

Meaning ▴ A VaR (Value at Risk) Model, within crypto investing and institutional options trading, is a quantitative risk management tool that estimates the maximum potential loss an investment portfolio or position could experience over a specified time horizon with a given probability (confidence level), under normal market conditions. </a><a href="https://prime.greeks.live/area/risk-factor/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract geometric design illustrating a central RFQ aggregation hub for institutional digital asset derivatives. Radiating lines symbolize high-fidelity execution via smart order routing across dark pools](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-high-fidelity-execution-architecture.jpg)

## Risk Factor

Meaning ▴ In the context of crypto investing, RFQ crypto, and institutional options trading, a Risk Factor is any identifiable event, condition, or exposure that, if realized, could adversely impact the value, security, or operational integrity of digital assets, investment portfolios, or trading strategies. </a><a href="https://prime.greeks.live/area/canonical-risk-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central RFQ aggregation engine radiates segments, symbolizing distinct liquidity pools and market makers. This depicts multi-dealer RFQ protocol orchestration for high-fidelity price discovery in digital asset derivatives, highlighting diverse counterparty risk profiles and algorithmic pricing grids](https://options.greeks.live/wp-content/uploads/2025/07/centralized-digital-asset-rfq-orchestration-for-multi-venue-liquidity-aggregation-and-high-fidelity-execution-across-diverse-counterparties.jpg)

## Canonical Risk Model

Meaning ▴ A Canonical Risk Model provides a standardized, foundational framework for systematically quantifying and managing financial risk across diverse assets and trading strategies within a financial system. </a>

**Tags:** [Risk Management](https://options.greeks.live/area/risk-management/), [ISDA SIMM](https://options.greeks.live/area/isda-simm/), [Portfolio Margining](https://options.greeks.live/area/portfolio-margining/), [Non-Cleared Derivatives](https://options.greeks.live/area/non-cleared-derivatives/), [Risk Data Taxonomy](https://options.greeks.live/area/risk-data-taxonomy/), [Risk Factor](https://options.greeks.live/area/risk-factor/)	

## Discover More

- 
### [What Is the Role of a Central Counterparty in Mitigating Risk for Multi-Leg Trades?](https://options.greeks.live/question/what-is-the-role-of-a-central-counterparty-in-mitigating-risk-for-multi-leg-trades/)
![A central, dynamic, multi-bladed mechanism visualizes Algorithmic Trading engines and Price Discovery for Digital Asset Derivatives. Flanked by sleek forms signifying Latent Liquidity and Capital Efficiency, it illustrates High-Fidelity Execution via RFQ Protocols within an Institutional Grade framework, minimizing Slippage.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-market-microstructure-rfq-protocol-optimization.jpg)

A central counterparty mitigates risk by replacing bilateral exposures with a single, netted, and guaranteed settlement system.

- 
### [What Are the Main Differences between SPAN and VaR Based Initial Margin Models?](https://options.greeks.live/question/what-are-the-main-differences-between-span-and-var-based-initial-margin-models/)
![A central blue sphere, representing a Liquidity Pool, balances on a white dome, the Prime RFQ. Perpendicular beige and teal arms, embodying RFQ protocols and Multi-Leg Spread strategies, extend to four peripheral blue elements. This depicts High-Fidelity Execution of Digital Asset Derivatives within complex Market Microstructure, optimizing Capital Efficiency and mitigating Counterparty Risk.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-rfq-protocol-execution-framework.jpg)

SPAN uses static scenarios for predictable margin, while VaR employs dynamic simulations for risk-sensitive capital efficiency.

- 
### [What Are the Capital Efficiency Benefits of Portfolio Margining for Complex Crypto Options Strategies?](https://options.greeks.live/question/what-are-the-capital-efficiency-benefits-of-portfolio-margining-for-complex-crypto-options-strategies/)
![Abstract geometric forms converge around a central RFQ protocol engine, symbolizing institutional digital asset derivatives trading. Transparent elements represent real-time market data and algorithmic execution paths, while solid panels denote principal liquidity and robust counterparty relationships. This illustrates high-fidelity execution and precise price discovery within complex market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-core-for-high-fidelity-execution.jpg)

Portfolio margining for crypto options optimizes capital deployment by aggregating net risk, liberating capital for enhanced strategic flexibility and superior returns.

- 
### [How Does Algorithmic Simplification Impact the Regulatory Acceptance of a Margin Calculation Model?](https://options.greeks.live/question/how-does-algorithmic-simplification-impact-the-regulatory-acceptance-of-a-margin-calculation-model/)
![Intersecting transparent and opaque geometric planes, symbolizing the intricate market microstructure of institutional digital asset derivatives. Visualizes high-fidelity execution and price discovery via RFQ protocols, demonstrating multi-leg spread strategies and dark liquidity for capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-market-microstructure-architecture.jpg)

Algorithmic simplification enhances regulatory acceptance by trading bespoke precision for transparent, defensible, and auditable system architecture.

- 
### [What Are the Primary Operational Hurdles in Mapping Internal Risk Factors to CCP VaR Models?](https://options.greeks.live/question/what-are-the-primary-operational-hurdles-in-mapping-internal-risk-factors-to-ccp-var-models/)
![Precision-engineered multi-layered architecture depicts institutional digital asset derivatives platforms, showcasing modularity for optimal liquidity aggregation and atomic settlement. This visualizes sophisticated RFQ protocols, enabling high-fidelity execution and robust pre-trade analytics.](https://options.greeks.live/wp-content/uploads/2025/08/modular-digital-asset-derivative-prime-os-for-institutional-rfq-and-block-trade-execution.jpg)

Mapping internal risk to CCP VaR models is an exercise in translating a bespoke risk language into a standardized, but often opaque, systemic utility.

- 
### [How Does the ISDA SIMM Model Reduce Overall Initial Margin Requirements?](https://options.greeks.live/question/how-does-the-isda-simm-model-reduce-overall-initial-margin-requirements/)
![A central RFQ aggregation engine radiates segments, symbolizing distinct liquidity pools and market makers. This depicts multi-dealer RFQ protocol orchestration for high-fidelity price discovery in digital asset derivatives, highlighting diverse counterparty risk profiles and algorithmic pricing grids.](https://options.greeks.live/wp-content/uploads/2025/07/centralized-digital-asset-rfq-orchestration-for-multi-venue-liquidity-aggregation-and-high-fidelity-execution-across-diverse-counterparties.jpg)

The ISDA SIMM model reduces initial margin by netting risk sensitivities across a standardized hierarchy of risk buckets and classes.

- 
### [Could Alternative Margin Models Effectively Balance Risk Sensitivity with Financial Stability?](https://options.greeks.live/question/could-alternative-margin-models-effectively-balance-risk-sensitivity-with-financial-stability/)
![An intricate mechanical assembly reveals the market microstructure of an institutional-grade RFQ protocol engine. It visualizes high-fidelity execution for digital asset derivatives block trades, managing counterparty risk and multi-leg spread strategies within a liquidity pool, embodying a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-architecture.jpg)

Alternative margin models balance risk sensitivity and financial stability by embedding counter-cyclical buffers and longer-term data into their core architecture.

- 
### [What Are the Primary Operational Challenges When Migrating to a Var-Based Margin System?](https://options.greeks.live/question/what-are-the-primary-operational-challenges-when-migrating-to-a-var-based-margin-system/)
![Sleek, dark grey mechanism, pivoted centrally, embodies an RFQ protocol engine for institutional digital asset derivatives. Diagonally intersecting planes of dark, beige, teal symbolize diverse liquidity pools and complex market microstructure. This highlights high-fidelity execution, price discovery, and atomic settlement via Prime RFQ for block trades.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-powering-high-fidelity-execution-for-multi-leg-spreads-portfolio-margin-and-atomic-settlement-via-prime-os-for-block-trades.jpg)

Migrating to a VaR-based margin system involves overcoming data, technology, and process challenges to achieve superior capital efficiency.

- 
### [The Strategic Imperative of Large Crypto Order Execution](https://options.greeks.live/guide/the-strategic-imperative-of-large-crypto-order-execution/)
![A sharp, dark, precision-engineered element, indicative of a targeted RFQ protocol for institutional digital asset derivatives, traverses a secure liquidity aggregation conduit. This interaction occurs within a robust market microstructure platform, symbolizing high-fidelity execution and atomic settlement under a Principal's operational framework for best execution.](https://options.greeks.live/wp-content/uploads/2025/07/principal-grade-digital-asset-derivatives-rfq-protocol-execution-engine.jpg)

Master large crypto orders with professional execution; dictate your market terms.

---

## Raw Schema Data

```json
{
    "@context": "https://schema.org",
    "@type": "BreadcrumbList",
    "itemListElement": [
        {
            "@type": "ListItem",
            "position": 1,
            "name": "Home",
            "item": "https://options.greeks.live"
        },
        {
            "@type": "ListItem",
            "position": 2,
            "name": "Question",
            "item": "https://options.greeks.live/question/"
        },
        {
            "@type": "ListItem",
            "position": 3,
            "name": "How Can Firms Standardize Risk Factors across Different Margin Models?",
            "item": "https://options.greeks.live/question/how-can-firms-standardize-risk-factors-across-different-margin-models/"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Organization",
    "@id": "https://greeks.live/#organization",
    "name": "Greeks.Live",
    "url": "https://greeks.live/",
    "description": "Greeks.live is the definitive toolkit for the professional options trader, dedicated to empowering our users with institutional-grade data, advanced analytics, and a comprehensive suite of trading tools designed to provide a decisive edge in the market. Every article and guide published under the Greeks.live name is a product of our deep commitment to data-driven analysis and our mission to support the global trading community.",
    "email": "support@greeks.live",
    "logo": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/Greeks.Live-Logo-Icon.svg"
    },
    "hasPart": [
        {
            "@type": "WebSite",
            "name": "About Us",
            "url": "https://about.greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "App Download Page",
            "url": "https://about.greeks.live/#/download/basic_trade"
        },
        {
            "@type": "WebSite",
            "name": "Newcomers' Guide",
            "url": "https://twisty-sign-c8b.notion.site/Guide-for-Greeks-live-X-Deribit-Newbies-5a08a3052822427d800af6eaf3704c8d"
        },
        {
            "@type": "WebSite",
            "name": "Knowledge Base",
            "url": "https://linktr.ee/greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "Option Terminology",
            "url": "https://greekslive.notion.site/88dfc00e7c584d4d8969b6302a79a12f?pvs=4"
        },
        {
            "@type": "WebSite",
            "name": "Terms of Service",
            "url": "https://greeks.live/#/tos"
        },
        {
            "@type": "WebSite",
            "name": "Disclaimer",
            "url": "https://greeks.live/#/disclaimer"
        }
    ],
    "sameAs": [
        "https://x.com/greekslive",
        "https://youtube.com/@greeks_live?si=-KjT0Lkw4WUCI0tD",
        "https://t.me/greekslive_notifications",
        "https://t.me/greekslive",
        "https://pinterest.com/greekslive/"
    ],
    "makesOffer": [
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "RFQ Platform",
                "url": "https://rfq.greeks.live",
                "description": "Execute institutional-grade block trades for crypto options and futures. This platform provides deep, over-the-counter (OTC) liquidity via a professional Request for Quote system.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Lending Tools",
                "url": "https://lend.greeks.live",
                "description": "Manage crypto lending and borrowing activities to earn yield or secure loans. This suite of tools helps users analyze interest rates and manage collateralized debt positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Data Lab",
                "url": "https://greeks.live/#/deribit/tools/datalab/BTC",
                "description": "Analyze historical and real-time options data with advanced metrics. This tool is designed for backtesting strategies and identifying market trends through comprehensive data visualization.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Profit & Loss Analysis (PnL)",
                "url": "https://greeks.live/#/deribit/tools/pnl_analysis",
                "description": "Track and analyze the Profit and Loss (PnL) of your options portfolio. This tool provides detailed performance metrics to help traders evaluate and refine their strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Position Visualization",
                "url": "https://greeks.live/#/deribit/tools/pv/BTC",
                "description": "Visualize complex options positions and their corresponding risk profiles. This tool generates payoff diagrams to help traders intuitively understand potential profit, loss, and exposure.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Options Calculator",
                "url": "https://greeks.live/#/deribit/tools/calculator",
                "description": "Calculate the theoretical price and risk parameters ('the Greeks') of crypto options. An essential tool for pricing new trades and analyzing the sensitivity of existing positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live Main Platform",
                "url": "https://greeks.live/",
                "description": "The central hub for the Greeks.live suite of professional cryptocurrency options and derivatives trading tools. Access analytics, manage positions, and explore the ecosystem.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Community Forum",
                "url": "https://t.greeks.live",
                "description": "Join the official Greeks.live community. Connect with other traders, share insights, and receive the latest updates directly from the team.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for Deribit",
                "url": "http://deribit.greeks.live",
                "description": "A dedicated interface for trading on the Deribit exchange. This platform leverages Greeks.live's advanced analytics and tools specifically for users of Deribit's options and futures markets.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for OKX",
                "url": "http://okx.greeks.live",
                "description": "A dedicated interface for trading on the OKX exchange. This platform integrates Greeks.live's powerful analytics with the OKX marketplace, focusing on its options and derivatives products.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "GLVS.AI Trading Platform",
                "url": "https://www.glvs.ai/",
                "description": "An AI-powered platform for automated crypto options trading. Leverage advanced algorithms and data-driven signals to develop and deploy sophisticated, hands-free trading strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "API Testnet & Sandbox",
                "url": "https://test.greeks.live",
                "description": "A dedicated testnet environment for developers to test their API integrations and automated trading strategies. This sandbox allows for full functionality testing without risking real funds.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        }
    ],
    "subOrganization": [
        {
            "@type": "Organization",
            "name": "GreeksLive HK Co., Limited",
            "email": "support@greeks.live",
            "telephone": "+852-56418949",
            "address": {
                "@type": "PostalAddress",
                "streetAddress": "Unit 11, 27/F, K. Wah Centre, 191 Java Road",
                "addressLocality": "Hong Kong",
                "addressRegion": "North Point",
                "addressCountry": "CN",
                "postalCode": "0000"
            },
            "geo": {
                "@type": "GeoCoordinates",
                "latitude": "22.2932357",
                "longitude": "114.2022026"
            },
            "image": "https://options.greeks.live/wp-content/uploads/2025/08/Wah-Centre-Hong-Kong.jpg"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Article",
    "mainEntityOfPage": {
        "@type": "WebPage",
        "@id": "https://options.greeks.live/question/how-can-firms-standardize-risk-factors-across-different-margin-models/"
    },
    "headline": "How Can Firms Standardize Risk Factors across Different Margin Models? \u25b4 Question",
    "description": "Firms standardize risk by building a canonical internal model that translates portfolio sensitivities into the unique inputs of each margin system. \u25b4 Question",
    "url": "https://options.greeks.live/question/how-can-firms-standardize-risk-factors-across-different-margin-models/",
    "datePublished": "2025-07-30T14:38:13+00:00",
    "dateModified": "2025-10-23T14:00:44+00:00",
    "articleSection": [
        "Question"
    ],
    "author": {
        "@id": "https://greeks.live/#organization"
    },
    "publisher": {
        "@id": "https://greeks.live/#organization"
    },
    "image": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/modular-rfq-protocol-segments-for-institutional-digital-asset-derivatives.jpg",
        "caption": "An abstract arc of interconnected, alternating pale green and dark grey segments, each light segment featuring a black dot. This visual metaphor represents the sophisticated market microstructure and RFQ protocol for institutional digital asset derivatives. Each segment signifies a distinct private quotation or aggregated inquiry phase, ensuring high-fidelity execution and atomic settlement. The modularity highlights a Prime RFQ framework, optimizing capital efficiency and best execution across liquidity pools for multi-leg spread strategies and portfolio margin management."
    },
    "keywords": [
        "Canonical Risk Model",
        "Capital Efficiency",
        "CCP Risk Management",
        "ISDA SIMM",
        "Margin Model",
        "Margin Model Reconciliation",
        "Margin Models",
        "Market Microstructure",
        "Non-Cleared Derivatives",
        "Portfolio Margining",
        "Risk Data Taxonomy",
        "Risk Factor",
        "Risk Factor Standardization",
        "Risk Factors",
        "Risk Management",
        "Sensitivity-Based Risk",
        "SPAN Margin",
        "Value-at-Risk Margin",
        "VaR Model"
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "WebSite",
    "url": "https://options.greeks.live/",
    "potentialAction": {
        "@type": "SearchAction",
        "target": "https://options.greeks.live/?s=search_term_string",
        "query-input": "required name=search_term_string"
    }
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "FAQPage",
    "mainEntity": [
        {
            "@type": "Question",
            "name": "How Do Different Models Define Risk?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "Understanding the fundamental differences in how margin models conceptualize risk is critical to forming a coherent strategy. Each model represents a different philosophy of risk measurement, and a standardization framework must be ableto bridge these philosophical divides. The table below compares the risk factor granularity and methodology of three dominant model types."
            }
        },
        {
            "@type": "Question",
            "name": "What Is The Core Transformation Logic?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "The transformation layer is where the theoretical strategy becomes a practical reality. It contains the rules-based and model-based logic for mapping. For instance, a firm's canonical model might have 50 granular interest rate tenor points for the USD curve. ISDA SIMM requires a specific set of 12 tenor buckets. The transformation logic would include an interpolation algorithm to distribute the risk from the 50 internal points to the 12 SIMM buckets. The table below illustrates a simplified mapping for a hypothetical portfolio's risk factors."
            }
        }
    ]
}
```


---

**Original URL:** https://options.greeks.live/question/how-can-firms-standardize-risk-factors-across-different-margin-models/
