How Does Span’S Use of Risk Arrays Differ from Tims’s Scenario Grid?

Concept

An institutional portfolio’s risk profile is a complex, multi-dimensional surface. The critical task for any clearinghouse or risk manager is to project this high-dimensional reality onto a single, actionable number ▴ the margin requirement. The architectural choice of how to perform this projection defines the operational reality for every market participant.

Two dominant design philosophies have shaped the landscape of derivatives margining, embodied by the Chicago Mercantile Exchange’s (CME) Standard Portfolio Analysis of Risk (SPAN) and the Options Clearing Corporation’s (OCC) Theoretical Intermarket Margin System (TIMS). Understanding their divergence begins with recognizing the fundamental problem they both solve ▴ quantifying potential loss for portfolios containing non-linear instruments like options and futures.

SPAN’s architecture is built upon a principle of standardized, pre-calculated risk components. At its core is the Risk Array, a set of numeric values generated by the exchange that represents the gain or loss for a single, specific contract under a predefined set of market shocks. Think of it as a lookup table. For each product it clears, the CME simulates a series of price and volatility movements and publishes the resulting profit or loss values in a data file.

A clearing firm’s system ingests this file, applies the P/L values from the array to its portfolio of positions, and identifies the scenario that produces the maximum loss. This loss, aggregated across the portfolio and adjusted for various spread credits and additional charges, becomes the margin. The risk array is a discrete, product-level component that forms the building block of a portfolio-level calculation.

TIMS, conversely, operates on a principle of holistic, portfolio-level revaluation. Its foundational element is the Scenario Grid. This grid defines a matrix of hypothetical market states, primarily focused on a range of potential prices for the underlying asset and shifts in the overall volatility surface. The TIMS process takes a member’s entire portfolio of positions and subjects it to a full re-pricing under each and every scenario in this grid.

It uses a theoretical options pricing model to calculate the portfolio’s value at each grid point. The margin requirement is determined by finding the single point on this grid that results in the greatest calculated loss for the portfolio as a whole. The scenario grid is a framework for dynamic valuation, where the risk of the portfolio is assessed as a single, integrated unit from the outset.

The core distinction lies in the unit of analysis ▴ SPAN builds a portfolio’s risk from pre-calculated, single-contract risk arrays, while TIMS evaluates the entire portfolio’s value across a grid of market scenarios.

This structural difference has profound implications. SPAN’s methodology, with its published risk parameter files, creates a highly predictable and replicable system. Any market participant with the SPAN software and the correct parameter file can calculate the exact same margin requirement as the exchange.

It is a system designed for immense scale and standardization, which is why it has been adopted by dozens of exchanges globally for a wide array of derivatives. TIMS’s approach is more computationally intensive but offers a potentially more nuanced view of risk for complex, inter-related equity option portfolios, as the simultaneous revaluation of all positions inherently captures the offsetting effects and non-linear correlations between them without the need for explicit “spread credit” rules.

Strategy

The strategic imperatives behind SPAN and TIMS reflect the markets they were designed to secure. The choice between a risk array architecture and a scenario grid architecture is a choice about the trade-offs between standardization, computational efficiency, and the granularity of risk modeling. These are not merely technical details; they are strategic decisions that shape capital efficiency, operational workflows, and the very nature of risk management for clearing members.

Architectural Philosophy and Strategic Intent

The strategic intent of CME SPAN is to provide a robust, transparent, and globally consistent framework for margining futures and options on futures. Its adoption by over 50 exchanges and clearing organizations is a testament to this strategy. The system is engineered for efficiency and predictability. By publishing a standardized Risk Array file each day, the CME Group provides all market participants with the exact building blocks needed to compute margin requirements.

This decouples the complex task of scenario generation (performed by the exchange) from the task of margin calculation (performed by the clearing firm). This strategy prioritizes a common language of risk, allowing for consistent application across diverse products and markets.

The strategy of OCC’s TIMS is rooted in the unique complexities of the U.S. listed equity options market. As the central clearer for this vast and intricate ecosystem, the OCC’s primary concern is the accurate modeling of large, complex portfolios of options with myriad strikes and expirations. The Scenario Grid strategy is designed to perform a holistic, top-down valuation.

It assesses the portfolio as an integrated entity, allowing the complex, non-linear interactions between different options positions to net out naturally within the valuation model itself. This approach is strategically tailored to capture the nuanced risk profile of sophisticated options strategies, such as multi-leg spreads and positions sensitive to shifts in the volatility skew, without relying on a system of pre-defined spread credits.

How Do the Methodologies Treat Key Risk Factors?

The strategic differences become tangible when examining how each system models financial risk. The table below juxtaposes their treatment of the primary risk vectors in a derivatives portfolio. This comparison reveals the architectural trade-offs inherent in each design.

Strategic Implications for Market Participants

For a clearing firm or large trader, the choice of methodology has direct strategic consequences. A firm primarily trading highly correlated futures contracts across different exchanges might find the SPAN methodology highly advantageous. The explicit inter-commodity spread credits provide a clear, quantifiable benefit that can be anticipated and incorporated into trading strategy costs. The predictability of the daily SPAN parameter file allows for precise, independent pre-trade margin calculations.

Conversely, an institution specializing in complex equity option overlays or volatility dispersion strategies might find the TIMS methodology to be a more accurate representation of its risk. The holistic revaluation approach can better capture the subtle correlations and volatility-skew effects that a rule-based spread credit system might miss. The strategic focus shifts from optimizing explicit spread credits to understanding how the entire portfolio’s valuation will behave under the various market states defined by the TIMS grid.

Execution

The execution of margin calculation under SPAN and TIMS translates their respective strategic philosophies into concrete operational workflows. For the institutional risk manager, understanding these procedural mechanics is essential for pre-trade analysis, capital management, and system integration. The difference is one of assembling pre-fabricated components versus commissioning a holistic analysis.

The Operational Playbook

The day-to-day process of calculating margin requirements follows a distinct path for each system, defined by the flow of data and the locus of computation.

CME SPAN Execution Workflow

The SPAN process is a distributed, multi-step procedure that relies on data provided by the exchange.

1. Parameter Generation ▴ Each trading day, the CME analyzes market data and determines the core risk parameters for each product it clears. These include the Price Scan Range (the expected maximum one-day price move), the Volatility Scan Range, and various spread parameters.
2. Risk Array Publication ▴ These parameters are used to compute the 16-scenario Risk Array for every single cleared contract. The array contains the precise profit or loss value for that contract under each scenario. This data is compiled into a single, large data file, the SPAN Risk Parameter File, and published for clearing members to download.
3. Portfolio Scan ▴ The clearing firm ingests the SPAN file into its risk system. The system then scans the firm’s portfolio. For each position, it looks up the corresponding P/L value for each of the 16 risk scenarios in the array. It then sums these values across all positions to find the total portfolio P/L for each scenario. The largest loss among these 16 totals is the “Scan Risk.”
4. Charge and Credit Application ▴ The system applies additional calculations. It identifies positions that qualify for Intra-Commodity (calendar) spread charges and Inter-Commodity spread credits, adjusting the total requirement accordingly. It also calculates a Short Option Minimum charge to account for deep out-of-the-money options risk. The final margin is the sum of these components.

OCC TIMS Execution Workflow

The TIMS process is a more centralized and holistic valuation exercise.

• Scenario Grid Definition ▴ The OCC defines and maintains a grid of market scenarios. This grid is multi-dimensional, specifying a range of underlying asset prices and corresponding implied volatility levels. This grid is less a public data file and more a set of instructions for the valuation engine.
• Portfolio Submission ▴ The clearing member submits its complete portfolio of positions to the OCC’s calculation engine or a proprietary system that replicates the OCC’s methodology.
• Holistic Revaluation ▴ The TIMS engine iterates through every point on the Scenario Grid. At each point (e.g. Underlying Price down 10%, Volatility up 5%), it uses a theoretical options pricing model to re-calculate the value of every single position in the portfolio and sums them to get a total portfolio value for that specific scenario.
• Worst-Case Loss Identification ▴ After revaluing the portfolio across all scenarios in the grid, the system identifies the single scenario that resulted in the lowest portfolio value. The difference between this value and the current market value of the portfolio represents the “worst-case loss,” which becomes the basis for the margin requirement. Broker-dealers may augment these scenarios with their own “house” scenarios to capture additional risks like extreme market moves or position concentration.

Quantitative Modeling and Data Analysis

To make the distinction concrete, we can visualize the core data structures of each system. The tables below are simplified representations, but they accurately reflect the structural difference between a Risk Array and a Scenario Grid.

A SPAN risk array is a list of P/L values for one contract, whereas a TIMS scenario grid is a map of valuation points for an entire portfolio.

Table ▴ Example of a CME SPAN Risk Array

This table shows a hypothetical Risk Array for a single S&P 500 e-mini futures contract (ES). The Price Scan Range is assumed to be 90 points ($4,500 per contract), and the Volatility Scan Range is 7%.

Table ▴ Visualization of an OCC TIMS Scenario Grid

This table visualizes the concept of the TIMS grid for an entire portfolio. The cells would contain the calculated Net P/L of the entire portfolio after re-pricing all instruments under that specific market condition.

In this TIMS example, the margin requirement would be based on the worst loss identified in the grid, which is -$1,250,000, occurring in the scenario where the underlying price drops by 10% and volatility increases by 10%.

Reflection

The examination of SPAN’s Risk Arrays and TIMS’s Scenario Grid moves beyond a simple comparison of two margining systems. It reveals a fundamental duality in risk management architecture ▴ the tension between a standardized, component-based model and a holistic, valuation-based model. Neither architecture is inherently superior; they are purpose-built systems designed with different strategic objectives to manage the risks of different market ecosystems.

The critical insight for an institutional operator is that the margining system is an active component of the market’s structure. It defines the cost of capital, influences strategic decisions, and sets the operational tempo for risk management. Your internal risk models and pre-trade analytics must be architected with a deep understanding of whether the clearinghouse uses a system of assembling pre-defined risk components or one of dynamic, holistic valuation.

Does your own operational framework treat risk as a series of additive parts or as the emergent property of an integrated portfolio? The answer dictates how effectively you can manage capital and navigate the complex topology of institutional risk.