What Are the Primary Differences in Risk Scenario Generation between SPAN and TIMS?

Concept

The fundamental architectural divergence between the Standard Portfolio Analysis of Risk (SPAN) and the Theoretical Intermarket Margin System (TIMS) originates from the markets they were engineered to secure. SPAN was conceived by an exchange, the Chicago Mercantile Exchange (CME), as a system to manage the component risks inherent in futures and options on futures portfolios. Its design philosophy is bottom-up, deconstructing portfolio risk into discrete, quantifiable factors such as price movement, volatility shifts, and time decay, which are then aggregated. This approach reflects the nature of futures markets, where risks like calendar spreads (basis risk between different contract months) are as significant as directional price exposure.

Conversely, TIMS was developed by a clearinghouse, The Options Clearing Corporation (OCC), to model risk for equity options and the underlying securities. Its architecture is top-down, applying a battery of holistic, theoretical price shocks to an entire portfolio of related instruments. The system evaluates the portfolio’s net liquidation value across a predefined grid of market outcomes.

This method is exceptionally well-suited for equity derivatives, where the primary risk driver is the price movement of a single underlying asset, and the complex interplay of options positions (e.g. spreads, combinations) can be assessed under these systemic shocks. The primary differences in their risk scenario generation are a direct consequence of these foundational design choices, tailored to the specific risk profiles of the asset classes they govern.

Strategy

Understanding the strategic application of SPAN and TIMS requires a comprehension of their core methodologies as reflections of different risk management philosophies. The choice between them, or the interaction with them, is dictated by the composition of a trading portfolio and the specific types of risk a firm must mitigate. Each system provides a distinct framework for capital efficiency and risk representation.

The Architectural Philosophy of SPAN

SPAN’s strategic strength lies in its granular, component-based approach to risk. It operates like a meticulous engineer disassembling a complex machine to inspect each part. The system does not just ask “what is the total loss?”; it asks “from where could the losses originate?”. The scenarios are built from these fundamental risk components.

SPAN’s design isolates specific risk factors, allowing for precise margining of complex futures and options strategies.

The primary components of SPAN’s risk calculation are:

• Scan Risk ▴ This is the system’s core. It calculates the potential loss of a portfolio by simulating a range of price and volatility movements. The “scenarios” are combinations of these shifts, creating a matrix of potential outcomes. For each instrument, the system uses a “risk array,” which is a data file specifying the expected gain or loss for each of these scenarios.
• Intra-Commodity Spreading ▴ SPAN recognizes that positions in different contract months of the same underlying commodity (e.g. long December Crude Oil, short June Crude Oil) have offsetting risks. It provides margin credits for these calendar spreads, acknowledging that the prices will not move in perfect lockstep, a phenomenon known as basis risk.
• Inter-Commodity Spreading ▴ The system extends this logic to related commodities (e.g. Crude Oil and Heating Oil). It provides partial margin relief for positions in different but correlated markets, based on historical price relationships.
• Short Option Minimum ▴ A floor margin is applied to deep out-of-the-money short options to account for the risk of a sudden, large market move (gamma risk) that might not be fully captured by the standard scenarios.

The strategy for a firm interacting with SPAN is to structure portfolios that maximize these offsets. Traders can build complex, multi-leg futures strategies knowing the margining system is designed to recognize the intricate risk relationships between the legs, leading to more efficient use of capital compared to a simple gross-margin system.

The Holistic Approach of TIMS

TIMS employs a different strategic lens. It is less concerned with dissecting risk into its atomic parts and more focused on the holistic behavior of a cluster of related securities under stress. Its scenarios are not built from independent risk factors but are defined as a set of fixed, theoretical price shocks applied to the underlying asset. The system then re-values all associated positions ▴ stock, ETFs, and options ▴ at each of these price points.

The strategic foundation of TIMS rests on the concept of “Class Groups” and “Product Groups.”

• Class Group ▴ This includes all securities that share the same underlying instrument. For example, all option series on Apple Inc. (AAPL) stock, along with AAPL stock itself, would form a single class group.
• Product Group ▴ This is a broader collection of highly correlated class groups. For instance, various ETFs tracking the S&P 500 index might be placed in the same product group.

The risk scenarios in TIMS are typically a series of equidistant price movements for the underlying asset. For example, the system might calculate the portfolio’s value if the underlying stock price moves up or down in 10 steps, covering a total range of +/- 15%. The largest calculated loss across all these scenarios for a given product group determines the margin requirement. A key aspect of TIMS is that it historically provided offsets only for positions within the same product group, a limitation that newer systems have sought to address.

What Is the Strategic Difference in Portfolio Management?

The strategic implications for portfolio managers are significant. A manager whose portfolio is margined under a SPAN-like system is incentivized to think about risk in terms of basis, volatility, and inter-market correlations. A manager under a TIMS-like system is incentivized to manage the consolidated delta, gamma, and vega exposures of their entire book of related securities. TIMS is highly effective for a typical equity options market maker, whose book consists of thousands of different option series all tied to a single underlying stock.

Comparative Framework

The following table outlines the strategic differences between the two methodologies.

Execution

The execution of risk scenario generation within SPAN and TIMS reveals their deep architectural differences. While both aim to secure portfolios by calculating a worst-case loss, their computational paths to that result are distinct. Understanding this execution layer is critical for any institution building trading systems, managing collateral, or developing risk models that interface with these core financial infrastructures.

The SPAN Execution Framework a Granular Calculation

The operational heart of SPAN is the Risk Parameter File , often called the SPAN file. Exchanges produce this file daily. It contains the Risk Arrays for every contract.

An array is a set of 16 numeric values representing the profit or loss for a single contract under 16 standardized scenarios. These scenarios are combinations of three factors ▴ price movement (the “price scan range”), volatility movement, and the passage of time.

The 16 core scenarios are typically structured as follows:

• Scenario 1 ▴ Unchanged price and volatility (the baseline).
• Scenario 2-3 ▴ Volatility up/down, price unchanged.
• Scenario 4-9 ▴ Price up/down by 1/3, 2/3, and 3/3 of the scan range, volatility unchanged.
• Scenario 10-15 ▴ Price up/down by 1/3, 2/3, and 3/3 of the scan range, combined with a move up or down in volatility.
• Scenario 16 ▴ An extreme price move, typically double the standard scan range, to cover “fat tail” risk.

The execution flow for a portfolio is a multi-step process:

1. Scan Risk Calculation ▴ For each combined commodity in the portfolio, the system calculates the profit or loss for each of the 16 scenarios by summing the P/L from the risk arrays of all contracts held. The largest loss among these 16 values is the initial Scan Risk.
2. Intra-Commodity Spread Charge ▴ The system then applies margin credits for calendar spreads. It has predefined rules for how much risk is offset between different months of the same future, reducing the total margin requirement.
3. Inter-Commodity Spread Credit ▴ A similar process occurs for correlated products (e.g. WTI Crude Oil vs. Brent Crude Oil). The SPAN file contains parameters for how much credit to give for offsetting positions across these commodities.
4. Net Margin Calculation ▴ The final requirement is the Scan Risk, adjusted for the spread credits, and then checked against a Short Option Minimum to ensure adequate coverage for gamma risk.

Hypothetical SPAN Risk Array Execution

Consider a hypothetical risk array for a single Gold (GC) futures contract. The exchange determines the price scan range is $60.

For a portfolio of 10 long GC contracts, the P/L for scenario 9 would be -$60,000. The system finds the maximum loss across all scenarios to set the margin.

The TIMS Execution Framework a Holistic Simulation

TIMS execution, particularly for Customer Portfolio Margin (CPM), is governed by parameters set by the OCC. It does not use component-based risk arrays in the same way as SPAN. Instead, it defines a series of market states and re-values the entire portfolio in each state.

The execution process for a portfolio within a specific “Product Group” is as follows:

1. Define Scenarios ▴ The system defines a set of theoretical price changes for the underlying asset. For individual stocks, this might be 10 equidistant points up and down to a maximum of +/- 15%. For broad-based indexes, the range might be +6% / -8%. Each of these price points is a distinct scenario.
2. Re-price Portfolio ▴ For each scenario, every instrument in the portfolio is re-valued. Stock positions are adjusted linearly. Options are more complex, requiring a pricing model (like Black-Scholes) to calculate their new theoretical value at the new underlying price and an implied volatility associated with that price level.
3. Calculate Net Loss ▴ The net profit or loss for the entire portfolio is calculated for each of the defined scenarios.
4. Determine Requirement ▴ The margin requirement for that product group is simply the single largest net loss found across all the tested scenarios. The total account margin is the sum of the requirements for each product group.

TIMS operates by simulating a grid of potential market prices and finding the point of maximum portfolio loss.

Hypothetical TIMS Scenario Execution

How Is A Simple Equity Portfolio Margined? Consider a portfolio with a single underlying stock, XYZ, currently trading at $100. The portfolio holds +1000 shares of XYZ and is short 10 call options with a strike price of $105. The TIMS model for this equity might test price shocks in $1.50 increments up to +/- $15.00 (a 15% range).

The table below shows a simplified calculation for a few of these scenarios. It assumes for simplicity that implied volatility does not change.

In this simplified example, the scenario generating the largest loss is Scenario 7 (a 15% drop in price), resulting in a loss of $13,000. This value would become the TIMS margin requirement for this portfolio, before any other account-level adjustments. The key is that the system directly calculates the net risk of the combined stock and option positions under a severe market shock, capturing the offsetting nature of the covered call strategy.

Reflection

The examination of SPAN and TIMS reveals two distinct architectures for systemic stability, each optimized for the products they govern. One is a granular, bottom-up assembly of risk components; the other is a top-down, holistic stress test. This architectural duality prompts a critical question for any trading institution ▴ Does your internal risk management framework operate as a component-based system, a holistic simulation, or a hybrid of the two?

Aligning your internal view of risk with the external margin methodologies of the clearing systems you depend on is a foundational element of capital efficiency and operational resilience. The ultimate edge lies not just in predicting the market, but in precisely modeling the systems that underpin it.