In What Circumstances Can a Firm Justify Using Internal Data over Third-Party Quotes in a Close-Out?

Concept

The decision to utilize proprietary data models over external quotations in a portfolio close-out is a function of analytical sovereignty. It represents a firm’s calculated assertion that its internal, dynamic understanding of an asset’s value, particularly under stressed or idiosyncratic conditions, provides a more accurate representation of executable reality than a polled, external consensus. This choice is predicated on the foundational belief that deep, proprietary data, curated over countless cycles of risk-taking and market-making, offers a structural advantage in moments of market dislocation or for assets characterized by inherent informational asymmetry.

A close-out is the terminal phase of a defaulted counterparty relationship. It is a forced liquidation, a final accounting driven by the imperative to neutralize risk and crystallize financial standing. In this context, valuation is the central mechanism. The objective is to arrive at a price that is fair, commercially reasonable, and defensible against subsequent legal or regulatory scrutiny.

The conventional path involves soliciting quotes from third-party dealers, a process designed to establish an objective market consensus. This method, rooted in the principle of observable market prices, serves as a bulwark against disputes, providing a clear, auditable trail of price discovery.

A firm’s reliance on its own data asserts that its internal view of risk and value is more precise than external market signals during critical liquidation events.

The justification for deviating from this established protocol arises when the very structure of the asset or the state of the market renders third-party quotes unreliable or even misleading. For highly illiquid, complex, or bespoke derivatives, a true market price may not be readily observable. The universe of potential buyers is small, and their pricing indications may be opportunistic or reflective of their own balance sheet constraints rather than the intrinsic value of the instrument. In such scenarios, external quotes become less a reflection of fair value and more a measure of transient market appetite, heavily skewed by the exigencies of the moment.

Here, the firm’s internal data transforms into a strategic asset. This data is a composite of historical transaction prices, proprietary volatility surfaces, correlation matrices, and model-derived analytics. It is the codified experience of the firm’s trading desk, a granular record of how similar instruments have behaved across a spectrum of market conditions.

When external liquidity evaporates and quoted bid-ask spreads widen to punitive levels, this internal data provides a stable, model-driven anchor for valuation. It allows the firm to calculate a close-out amount based on a more fundamental, through-the-cycle assessment of the asset’s worth, insulated from the panic or opportunism that can grip external market makers during a default event.

Strategy

The strategic decision to employ internal data in a close-out is an exercise in calibrated risk management, weighing the defensibility of internal models against the potential fallibility of external markets. This choice is governed by a framework that assesses asset characteristics, market conditions, and the robustness of the firm’s own valuation architecture. The strategy is built upon identifying specific, pre-defined triggers where the integrity of third-party quotes is compromised, thereby making a turn to internal, model-based valuation not just preferable, but necessary for achieving a commercially reasonable outcome.

When Are External Quotes Unreliable?

A core component of this strategy is the systematic identification of market and asset conditions that degrade the quality of third-party quotes. A firm must develop a clear taxonomy of situations where the informational content of external prices is likely to be low. This allows the institution to move from a reactive to a proactive stance, justifying the use of internal data based on a pre-agreed and documented policy.

• Asset Complexity and Illiquidity For instruments like long-dated, multi-underlying exotic derivatives or structured credit products tailored to a specific client’s needs, a broad and liquid market rarely exists. The number of dealers capable of pricing, let alone warehousing, such risk is exceptionally small. In a close-out, the few quotes obtained are likely to be heavily discounted for model risk, hedging costs, and balance sheet impact, bearing little resemblance to a theoretical fair value.
• Systemic Market Stress During a market-wide crisis, such as a major credit event or a sovereign default, interdealer liquidity contracts violently. Bid-ask spreads on even relatively standard instruments can widen to levels that make execution prohibitive. Third-party dealers, focused on de-risking their own books, will provide quotes that are defensive and designed to discourage winning the asset. These prices reflect a temporary, acute aversion to risk rather than a sustainable valuation.
• Idiosyncratic Default Events The default of a major counterparty can itself contaminate the pricing process. Other dealers may have correlated exposures to the defaulting entity, causing them to price all related assets with extreme prejudice. The close-out of a large, concentrated position can temporarily overwhelm the market’s absorptive capacity, leading to price quotes that reflect a short-term supply-demand imbalance. In this environment, internal models can provide a more stable measure of value, filtering out the noise of the immediate default event.

Building a Defensible Internal Valuation Framework

A firm cannot simply decide to use its own numbers without a rigorous, auditable, and consistent framework. The strategy must be underpinned by an infrastructure that can withstand scrutiny from auditors, regulators, and potentially, the courts. This involves several key pillars.

The table below outlines a comparative framework for deciding between third-party quotes and internal data, based on key operational and risk factors.

This framework provides a structured decision-making process. It moves the choice away from a subjective judgment call made under duress and toward a policy-driven determination. The ultimate goal is to create a valuation process that is not only economically sound but also legally and regulatorily robust.

Execution

The execution of a close-out using internal data is a high-stakes procedural exercise. It demands a seamless integration of legal interpretation, quantitative modeling, and operational precision. The process must be meticulously documented and executed with the assumption that it will be challenged, requiring a complete and transparent audit trail. The firm’s ability to defend its valuation hinges on the rigor of this execution.

The Operational Playbook for Internal Valuation

A firm must establish a clear, sequential process for invoking internal data valuation. This playbook ensures consistency and defensibility, removing ambiguity from a time-sensitive and contentious procedure.

1. Trigger Event Confirmation The first step is the formal declaration of an Event of Default under the terms of the governing agreement, such as an ISDA Master Agreement. The legal and credit risk teams must confirm the trigger and issue an internal directive to the valuation and trading teams to commence the close-out process.
2. Market Sounding and Quote Integrity Test Even when internal data is anticipated, the firm must make a good-faith effort to obtain external quotes. This is a critical step for defensibility. The firm should solicit quotes from an approved list of dealers. The responses, or lack thereof, are documented. Each received quote is then assessed against a set of integrity criteria ▴ Is it firm or indicative? What is the bid-ask spread? How does it compare to recent transaction data? This process creates the evidentiary basis for deeming external quotes unreliable.
3. Invocation of Internal Model Protocol Once the market sounding is complete and the results justify moving away from external quotes, the Head of Trading or a designated senior manager formally invokes the internal model protocol. This decision, along with the supporting evidence from the quote integrity test, is logged in a permanent record.
4. Model Selection and Data Input The appropriate, pre-approved valuation model for the specific asset class is selected. The model inputs (e.g. volatility, correlation, interest rate curves) must be sourced from the firm’s official, end-of-day data repositories. The use of ad-hoc or unverified data is strictly prohibited. The data snapshot used for the calculation must be time-stamped to the moment of the close-out calculation.
5. Independent Calculation and Verification The valuation is performed by the primary risk or trading desk. Concurrently, a separate, independent team ▴ such as a model validation group or a middle office function ▴ re-calculates the valuation using the same model and data inputs. This four-eyes verification process is essential for identifying errors and demonstrating procedural integrity. Any material discrepancy between the two calculations must be reconciled and explained before the final value is adopted.
6. Finalization and Notification The verified close-out amount is formally recorded and communicated to the defaulted counterparty in accordance with the notice provisions of the governing agreement. The notification should include a statement of the amount, the date of calculation, and a summary of the accounts being closed out. A detailed breakdown of the valuation methodology is typically reserved for subsequent discussions or disputes.

Quantitative Modeling and Data Analysis

The credibility of the entire process rests on the quality of the quantitative models and the data that feeds them. A firm must be able to demonstrate that its models are not just theoretical constructs but are actively used, tested, and validated in the normal course of business.

The following table provides a simplified example of the data inputs and model outputs for valuing a complex interest rate derivative during a close-out, contrasting it with unreliable market quotes.

This data demonstrates a situation where the external market is showing clear signs of distress (a 40-basis-point spread on a swap rate is exceptionally wide). The internal model, relying on validated, consistently applied data, produces a valuation that is significantly different. The ability to produce this level of detail, showing how each component of the valuation was derived from a trusted internal source, is the cornerstone of a defensible execution.

What Is the Legal Basis for Using Internal Data?

The justification for using internal data is often embedded within the contractual language of standard financial agreements. The ISDA Master Agreement, a cornerstone of the OTC derivatives market, provides for different methods of calculating close-out amounts. While the “Market Quotation” method relies on third-party dealer quotes, the “Loss” method allows a party to determine its total losses and costs resulting from the termination in a commercially reasonable manner.

When market quotes are unavailable or demonstrably unreasonable, a firm can argue that using its own consistently applied and validated internal models is the most commercially reasonable way to determine its true economic loss. The key is the ability to prove that the internal calculation was performed in good faith and reflects a value that could have been realized over time, absent the fire-sale conditions of a default.

Reflection

Is Your Valuation Process an Asset or a Liability?

The decision to rely on internal data during a close-out moves a firm’s valuation framework from a passive accounting function to an active component of its risk management architecture. It forces a critical self-examination. Does your firm possess a body of proprietary data deep enough to be considered a strategic asset?

Are your models sufficiently robust and independently validated to withstand the intense scrutiny of a legal challenge? The integrity of a close-out is a reflection of the integrity of the firm’s entire risk infrastructure.

Viewing valuation through this lens transforms the conversation. It becomes a continuous process of curating data, refining models, and stress-testing procedures, ensuring that when a counterparty defaults, the firm can act with precision and authority. The ultimate strength of a financial institution is revealed not in calm markets, but in its capacity to manage failure. A defensible, data-driven close-out process is a clear demonstration of that capacity.