# What Is the Role of Internal Models versus Third Party Quotes in a Volatile Market Close Out? ▴ Question

**Published:** 2025-08-02
**Categories:** Question

---

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## Concept

The moment a counterparty defaults on a derivatives contract during a period of extreme market turbulence is the ultimate validation of a firm’s entire risk architecture. This event crystallizes a fundamental tension at the heart of financial engineering. The question becomes what is the true, defensible value of a terminated position when observable market prices are erratic, unreliable, or have vanished entirely?

The answer resides in the dynamic relationship between two distinct pillars of valuation ▴ the [internal model](https://prime.greeks.live/area/internal-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and the third-party quote. Understanding their respective roles is a prerequisite for navigating a close-out, which is the process of terminating and settling all outstanding contracts with the defaulting entity.

An internal model represents the institution’s proprietary, calculated understanding of risk and value. It is a sophisticated system built upon a foundation of mathematical formulas, historical data, and carefully calibrated assumptions about future market behavior. Its primary function is to generate a consistent, forward-looking measure of [potential future exposure](https://prime.greeks.live/question/how-does-the-net-to-gross-ratio-impact-the-potential-future-exposure-calculation-under-the-basel-framework/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (PFE), which quantifies the maximum expected loss at a future date with a certain degree of confidence. This model is the institution’s internal compass, providing a stable valuation reference that is insulated from the immediate panic of a [volatile market](https://prime.greeks.live/area/volatile-market/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

It operates based on the firm’s own established risk parameters and methodologies, often approved and scrutinized by regulators for determining capital adequacy. This regulatory acceptance grants the internal model a significant degree of authority, as it forms the basis for the firm’s safety buffers against unexpected losses.

> A firm’s internal model provides a consistent valuation framework, while third-party quotes offer a direct, albeit sometimes volatile, connection to the live market.

In direct contrast stands the third-party quote. This is the external, observable, and executable price for a given instrument, solicited from market makers, dealers, or inter-dealer brokers. Its role is to provide a “mark-to-market” valuation, reflecting the price at which a position could theoretically be replaced or unwound in the current environment.

In stable, liquid markets, the values generated by [internal models](https://prime.greeks.live/area/internal-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and the prices obtained from [third-party quotes](https://prime.greeks.live/area/third-party-quotes/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) tend to converge, existing in a state of close alignment. The external market validates the model’s assumptions, and the model confirms that market prices are rational.

Extreme volatility acts as a powerful catalyst, shattering this alignment. As uncertainty surges, liquidity evaporates. The VIX index, a measure of expected future volatility, may spike, indicating widespread fear. In such conditions, dealers providing third-party quotes face immense challenges.

Their own risk limits are breached, information asymmetry becomes a paramount concern, and the bid-ask spreads they are willing to offer widen dramatically to compensate for the elevated danger. Quotes become scarce, erratic, and potentially reflect distressed selling rather than fair value. This is the precise moment the close-out must occur, and the divergence between the stable, model-driven internal value and the chaotic, quote-driven external price reaches its apex. The core of the challenge is therefore not simply choosing one over the other, but designing a system that can intelligently synthesize these two disparate sources of information to arrive at a valuation that is both financially sound and legally defensible.

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## Strategy

The strategic handling of a volatile market close-out demands a pre-defined, robust framework that anticipates the divergence between internal calculations and external market data. A successful strategy is one that establishes a clear hierarchy of valuation sources, calibrated to market conditions and grounded in the legal realities of the underlying contractual agreements, typically the [ISDA Master Agreement](https://prime.greeks.live/area/isda-master-agreement/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The objective is to produce a close-out amount that is [commercially reasonable](https://prime.greeks.live/area/commercially-reasonable/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and minimizes the potential for protracted legal disputes.

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## The Internal Model as the Strategic Anchor

One strategic approach grants primacy to the internal model as the foundational anchor for valuation. This perspective views the internal model as the most stable and consistent measure of value, particularly when external quotes are compromised by illiquidity and panic. The model’s strength lies in its ability to look through the noise of the market and calculate a value based on long-term fundamentals and pre-established risk parameters. It is a product of the firm’s own intellectual capital and risk appetite, designed to be a reliable guide in all market conditions.

The use of internal models like the Internal Model Method (IMM) for calculating [counterparty credit risk](https://prime.greeks.live/area/counterparty-credit-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) exposure is a well-established practice, accepted by supervisors for determining regulatory capital. This provides a strong basis for its use as a primary tool in a close-out scenario.

This strategy involves using the model’s output as the baseline valuation, which is then cross-referenced against any available [market data](https://prime.greeks.live/area/market-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The institution trusts its own sophisticated quantitative analysis over what it may perceive as fleeting and irrational market behavior. This approach provides a consistent methodology that prevents the firm from being whipsawed by erratic price swings and protects it from being forced to accept a valuation from a counterparty that may be in a distressed state. The strategic advantage is control and consistency in the face of external chaos.

### Table 1 ▴ Internal Model Components

| Model Component | Core Data Inputs | Primary Function | Strategic Advantage |
| --- | --- | --- | --- |
| Potential Future Exposure (PFE) Engine | Trade parameters, historical volatility, correlation matrices | Simulates thousands of potential future market scenarios to calculate maximum potential loss at a given confidence level. | Provides a forward-looking, statistically robust measure of risk, independent of current market sentiment. |
| Credit Valuation Adjustment (CVA) Calculator | Counterparty credit spreads, probability of default (PD), loss given default (LGD) | Quantifies the market value of counterparty default risk, allowing for a more precise pricing of derivatives. | Ensures that the risk of default is explicitly priced into the portfolio’s value before a close-out event occurs. |
| Volatility Surface Generator | Real-time options prices, historical price data | Creates a three-dimensional map of implied volatilities across various strike prices and expiration dates. | Enables more accurate pricing of options and other volatility-sensitive instruments within the model. |
| Data Calibration & Backtesting Module | Historical market data, past P&L | Continuously adjusts model parameters to align with observed market behavior and tests the model’s predictive accuracy. | Maintains the model’s relevance and reliability over time, building confidence in its outputs during a crisis. |

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## External Verification as the Reality Check

A contrasting strategy emphasizes the indispensability of external verification. This viewpoint posits that an internal model, no matter how sophisticated, is ultimately a theoretical construct. Without a connection to the real, executable market, its calculations are open to challenge. Third-party quotes, even if imperfect, represent the most tangible evidence of market value at a specific point in time.

Legal agreements, such as the ISDA [Master Agreement](https://prime.greeks.live/area/master-agreement/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), often contain language requiring that valuations be “commercially reasonable,” a standard that is difficult to meet without referencing external market data. Relying solely on an internal model risks creating a valuation that, while internally consistent, is disconnected from reality and could be successfully disputed by the defaulting counterparty’s administrators.

The execution of this strategy involves a disciplined process of soliciting quotes from a pre-vetted list of market makers. The challenge, especially in volatile markets, is the potential for a lack of reliable quotes. As research on credit derivatives has shown, the risk of [insider trading](https://prime.greeks.live/area/insider-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) or severe information asymmetry can cause liquidity to evaporate precisely when it is most needed.

This can lead to a situation where the few available quotes are from distressed sellers or are so wide as to be meaningless. Therefore, a strategy reliant on external quotes must also include a clear fallback protocol.

- **Tier 1 Solicitation** The first step involves requesting firm quotes from a primary list of at least three to five pre-approved, creditworthy dealers with whom the institution has a strong relationship.

- **Tier 2 Expansion** If fewer than the desired number of quotes are returned, the solicitation process is expanded to a secondary list of market participants to ensure a wider polling of the market.

- **Tier 3 Indicative Data** In the absence of firm, executable quotes, the institution may turn to indicative quotes, consensus pricing services, or other observable market data points to construct a view of fair value.

- **Tier 4 Model Fallback** Only after exhausting all reasonable efforts to obtain external data does the institution fall back to its internal model, documenting every step taken to prove that the market was unable to provide a reliable price.

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## How Does a Hybrid Approach Create Resilience?

The most robust and defensible strategy is a calibrated synthesis of the two approaches. This hybrid framework uses the internal model to establish a quantitative, objective valuation baseline while simultaneously using the third-party quote process as a crucial mechanism for market validation and price discovery. The internal model does not dictate the final price, but it creates a “zone of reasonableness” against which external quotes can be judged.

In this integrated system, the close-out process begins with the internal model generating a preliminary valuation. The firm then executes its quote solicitation protocol. The received quotes are analyzed not just in relation to each other, but also in relation to the model’s baseline. Quotes that fall within a pre-defined tolerance band (e.g.

+/- 2% of the model’s value) are considered valid and are typically averaged to determine the final [close-out amount](https://prime.greeks.live/area/close-out-amount/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). A quote that falls significantly outside this band acts as a red flag, triggering an investigation. It could indicate a flawed model assumption, a “fat finger” error from the dealer, or, most critically, a market dislocation that the model has not yet captured. This process transforms the valuation from a simple choice between two sources into an interactive and dynamic process of reconciliation, creating a final number that is supported by both internal logic and external evidence.

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## Execution

The execution of a close-out in a volatile market is a high-stakes, time-sensitive procedure that demands operational precision and a deep understanding of the underlying legal and quantitative frameworks. A successful execution is the result of meticulous preparation that begins long before an event of default is ever triggered. It requires a robust technological architecture, clearly defined procedural playbooks, and a sophisticated approach to quantitative analysis.

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## The Operational Playbook

An institution’s ability to execute a seamless close-out is directly proportional to the quality of its operational playbook. This playbook is a living document that details every step of the process, ensuring that actions taken during a crisis are systematic and defensible, not ad-hoc and panicked.

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## Pre-Emptive Architectural Design

The foundation of the playbook is the architectural work done during the ordinary course of business. This involves careful negotiation of legal agreements and the construction of the necessary technological infrastructure.

A critical component is the Schedule to the ISDA Master Agreement. This document is where counterparties define the specific mechanics of a close-out. The choice between the “Market Quotation” and “Loss” methods of valuation is paramount. Market Quotation prescribes a more rigid process centered on obtaining quotes from reference market makers.

The Loss method provides more flexibility, allowing the non-defaulting party to calculate its total losses and costs resulting from the termination in good faith. In a volatile market, the Loss method may provide more latitude to incorporate internal model data, but it also requires a higher burden of proof to demonstrate that the calculated loss was commercially reasonable. The playbook must specify the exact procedures to be followed for the method chosen in each specific counterparty agreement.

Simultaneously, the firm must build and maintain its internal valuation infrastructure. This is not a single piece of software but a complex ecosystem of systems. It includes data feeds for all relevant market variables (interest rates, FX rates, credit spreads, volatilities), a powerful calculation engine capable of running complex simulations like Monte Carlo analysis for PFE, and a CVA desk that constantly marks the portfolio to account for changes in counterparty credit quality. The models must be subject to rigorous, independent validation and regular backtesting to ensure their accuracy and to satisfy regulatory requirements.

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## The Close-Out Execution Protocol

When an event of default occurs, the playbook dictates a precise sequence of actions. This ensures a controlled and auditable response.

- **Event of Default Notification** The first step is the formal delivery of a notice of an Event of Default to the counterparty, which officially triggers the close-out process. This is a legal step with precise requirements.

- **Designation of [Early Termination Date](https://prime.greeks.live/area/early-termination-date/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** The non-defaulting party designates an Early Termination Date. This is the critical “as of” date for which all valuations will be calculated. The date chosen must be commercially reasonable and as soon as practicable after the default.

- **Internal Model Snapshot** The firm’s quantitative team takes a snapshot of the portfolio and runs the internal models to calculate the baseline close-out amount as of the Early Termination Date. This provides the initial, internally-validated number.

- **Quote Solicitation** The operations team initiates the dealer polling process as outlined in the strategy. They send standardized request for quote (RFQ) messages to the approved list of dealers, specifying the exact transactions and the valuation date. All communications are logged.

- **Reconciliation and Determination** The received quotes are compiled and analyzed. The playbook will contain specific rules for this process, such as automatically discarding the highest and lowest quotes to remove outliers, or calculating a volume-weighted average price if trade sizes are available. This reconciled market value is then compared to the internal model’s value. If the divergence is within the pre-set tolerance, the market value is used. If it exceeds the tolerance, a senior review committee is convened to make a final determination, which may involve blending the two values or siding with the model if the market is deemed too dysfunctional.

- **Settlement Statement** A detailed statement is prepared and sent to the defaulting counterparty, showing the valuations of all terminated transactions and the calculation of the final net settlement amount. This documentation is crucial for demonstrating that the process was fair and transparent.

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## Quantitative Modeling and Data Analysis

The credibility of the entire execution process rests on the quantitative integrity of the models and the analytical rigor applied to the data. This is where the theoretical strategy is translated into concrete numbers.

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## How Is Potential Future Exposure Modeled?

[Potential Future](https://prime.greeks.live/question/how-does-the-net-to-gross-ratio-impact-the-potential-future-exposure-calculation-under-the-basel-framework/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Exposure (PFE) is a cornerstone of the internal model. It is typically calculated using Monte Carlo simulation, which projects thousands of possible paths for relevant market factors. For each path and at each future time step, the portfolio is re-valued. The exposure on any given path is the replacement cost if the counterparty were to default at that point, which is the greater of the portfolio’s value or zero.

The PFE at a certain confidence level (e.g. 99%) is the value that is not expected to be exceeded in 99% of all simulated paths.

### Table 2 ▴ Simplified PFE Calculation For An Interest Rate Swap

| Simulation Path | Time Step (Months) | Simulated 3M LIBOR | Swap Mark-to-Market ($) | Exposure (max(MTM, 0)) |
| --- | --- | --- | --- | --- |
| 1 | 6 | 2.10% | +50,000 | 50,000 |
| 1 | 12 | 2.25% | +75,000 | 75,000 |
| 2 | 6 | 1.80% | -15,000 | 0 |
| 2 | 12 | 1.75% | -22,000 | 0 |
| . (998 more paths) | . | . | . | . |

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## Data Reconciliation in Practice

When third-party quotes are received, they are subjected to a rigorous data analysis process. The goal is to identify and handle outliers that could skew the final valuation. This is not a subjective exercise but one based on statistical measures.

### Table 3 ▴ Quote Reconciliation And Outlier Detection

| Dealer ID | Quote Received ($) | Deviation from Mean (%) | Z-Score | Status |
| --- | --- | --- | --- | --- |
| Dealer A | 1,520,000 | +1.33% | +0.85 | Accept |
| Dealer B | 1,505,000 | +0.33% | +0.21 | Accept |
| Dealer C | 1,480,000 | -1.33% | -0.85 | Accept |
| Dealer D | 1,650,000 | +10.00% | +6.36 | Review (Outlier) |
| Dealer E | 1,495,000 | -0.33% | -0.21 | Accept |

> In this example, the mean of the quotes is calculated, and the standard deviation is used to generate a Z-score for each quote. A quote with a Z-score above a certain threshold (e.g. 2.0 or 3.0) is flagged as a statistical outlier requiring manual investigation.
Dealer D’s quote would be excluded from the initial average and investigated to determine the cause of the deviation. This documented, rules-based approach is essential for defending the final close-out amount.

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## What Are the System Integration Requirements?

Executing this strategy effectively requires seamless technological integration. The risk management system, which houses the internal models, must have real-time data links to the trading platforms and the core settlement systems. When a close-out is triggered, trade data must flow automatically into the valuation engines. The RFQ process should be managed through an electronic platform that can broadcast requests, receive quotes, and log all activity for audit purposes.

The reconciliation tools should be integrated into the same workflow, allowing for a swift and efficient comparison of internal and external data. The entire architecture must be designed for resilience and speed, because in a volatile market close-out, every minute counts.

![An institutional grade system component, featuring a reflective intelligence layer lens, symbolizes high-fidelity execution and market microstructure insight. This enables price discovery for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-precision-rfq-execution-system-interface.jpg)

## References

- Gregory, Jon. _The xVA Challenge ▴ Counterparty Credit Risk, Funding, Collateral, and Capital_. 4th ed. Wiley, 2020.

- Hull, John C. _Options, Futures, and Other Derivatives_. 11th ed. Pearson, 2021.

- Acharya, Viral V. and Timothy C. Johnson. “Insider Trading in Credit Derivatives.” _Journal of Financial Economics_, vol. 84, no. 1, 2007, pp. 110-141.

- European Central Bank. “Guide to the Assessment of Internal CCR Models.” _ECB Banking Supervision_, Nov. 2020.

- Caporale, Guglielmo Maria, et al. “Aggregate Insider Trading and Stock Market Volatility in the UK.” _CESifo Working Paper No. 10502_, June 2023.

- ISDA. “ISDA Master Agreement.” _International Swaps and Derivatives Association_, 2002.

- O’Hara, Maureen. _Market Microstructure Theory_. Blackwell Publishers, 1995.

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## Reflection

![Intricate circuit boards and a precision metallic component depict the core technological infrastructure for Institutional Digital Asset Derivatives trading. This embodies high-fidelity execution and atomic settlement through sophisticated market microstructure, facilitating RFQ protocols for private quotation and block trade liquidity within a Crypto Derivatives OS](https://options.greeks.live/wp-content/uploads/2025/08/institutional-crypto-derivatives-execution-system-microstructure.jpg)

## Calibrating Your Institutional Architecture

The successful navigation of a volatile market close-out is the ultimate expression of an institution’s risk culture and operational preparedness. The frameworks and protocols discussed here provide a systemic view, but their true value is realized only when they are applied to your own specific architecture. The knowledge gained should prompt a critical self-assessment.

How is your institution’s ISDA schedule structured for valuation, and does it provide the necessary flexibility in a crisis? Is your internal model a dynamic, validated engine for risk discovery, or is it a static utility for regulatory reporting?

Consider the strength and diversity of your third-party quoting network. Have you tested its responsiveness under stressed conditions? The true measure of a valuation framework is its resilience at the point of maximum pressure.

Viewing the interplay between internal models and external quotes as a dynamic system of checks and balances, rather than a simple choice, is the first step toward building an operational framework that does not just survive a close-out but executes it with precision and authority. The ultimate strategic edge is found in an architecture that is robust, responsive, and, above all, defensible.

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---

**Original URL:** https://options.greeks.live/question/what-is-the-role-of-internal-models-versus-third-party-quotes-in-a-volatile-market-close-out/
