How Do You Quantify Best Execution for an Illiquid OTC Derivative?

Concept

Quantifying best execution for an illiquid Over-the-Counter (OTC) derivative is an exercise in constructing a defensible evidentiary framework. It moves beyond the search for a single, definitive price benchmark, a luxury afforded by liquid, exchange-traded instruments. For these bespoke contracts, negotiated in opaque environments, the very idea of a universally agreed-upon price at any given moment is a fiction. Therefore, the quantification of execution quality becomes a multi-dimensional assessment of process, data, and judgment.

The core challenge resides in the structural nature of these markets ▴ they are, by design, fragmented and private. This necessitates a shift in perspective from simple price comparison to a sophisticated, audit-proof demonstration that all sufficient steps were taken to achieve a favorable outcome for the client.

The foundation of this framework is the acknowledgment that in the absence of a continuous, visible tape, the “best” outcome is a range of possibilities, not a single point. Regulatory mandates, such as those within MiFID II, recognize this inherent ambiguity and consequently place emphasis on the integrity of the execution process itself. The obligation is to demonstrate that the chosen execution strategy was sound, the selection of counterparties was logical, and the final negotiated terms were reasonable given the prevailing market conditions, the specific characteristics of the instrument, and the client’s objectives. This reasonableness is established through a rigorous process of data capture and analysis, both before and after the trade is executed.

The challenge of quantifying execution for illiquid derivatives lies in substituting the unavailable public benchmark with a robust, internal process of evidence collection and analysis.

This process begins with a deep understanding of the execution factors at play. While price is paramount, for illiquid instruments, factors like likelihood of execution, settlement risk, and the management of information leakage gain significant weight. A low price from an unreliable counterparty or a price that is only available for a fraction of the desired size may not represent the best outcome.

The quantification, therefore, involves assigning a qualitative and, where possible, quantitative assessment to each of these factors. It is an act of building a case, supported by data, that the chosen path through a fragmented liquidity landscape was the most prudent one available at that specific moment in time.

Ultimately, the system for quantifying best execution in this context is a defensive one. It is designed to withstand regulatory scrutiny and internal audit by providing a clear, logical, and data-supported narrative of each trade. This narrative replaces the simple “price beat the benchmark” report of the equities world with a more complex, yet more meaningful, story of professional diligence in an inherently uncertain environment. The focus is on the quality of the decision-making process, which, in the world of illiquid derivatives, is the most reliable proxy for the quality of the outcome itself.

Strategy

Developing a strategy to quantify best execution for illiquid OTC derivatives requires the formalization of a multi-stage analytical process. This process serves to create a durable, auditable record that substantiates the quality of each execution. The strategy can be deconstructed into three core pillars ▴ Pre-Trade Intelligence, At-Trade Protocol, and Post-Trade Forensics. Each pillar is supported by a foundation of robust data management and proprietary modeling, which collectively serve to construct an internal, defensible benchmark where no public one exists.

The Three Pillars of a Defensible Framework

The initial pillar, Pre-Trade Intelligence, involves gathering and analyzing all available data to establish a “reasonableness corridor” for the potential transaction. This is not about predicting the exact price but about defining the boundaries of a fair outcome. Key activities in this stage include:

• Model-Based Pricing ▴ Developing or subscribing to an independent valuation model for the specific derivative type. This model, fed with real-time market data inputs (e.g. underlying asset prices, volatility surfaces, interest rate curves), produces a theoretical “mid-market” price. This becomes the primary internal reference point against which quotes will be measured.
• Market Condition Analysis ▴ Documenting the prevailing market environment at the time of the inquiry. This includes metrics of volatility, liquidity in related assets, and any significant market events. This context is vital for justifying the eventual execution level; a wider spread might be deemed reasonable during a period of high market stress.
• Counterparty Assessment ▴ Systematically evaluating potential counterparties based on predefined criteria. This extends beyond just credit risk to include historical responsiveness, settlement efficiency, and past pricing competitiveness on similar instruments.

The second pillar, At-Trade Protocol, focuses on the disciplined execution of the transaction itself, ensuring the process is fair, competitive, and minimizes information leakage. The Request for Quote (RFQ) process is central to this pillar. A robust RFQ protocol involves soliciting quotes from a curated list of approved counterparties.

The strategy here dictates that the number and selection of counterparties should be appropriate for the size and complexity of the trade, balancing the need for competitive tension with the risk of revealing trading intentions too broadly. All quotes received, along with the precise time stamps, are meticulously logged against the pre-trade model price.

A disciplined at-trade protocol transforms the subjective process of negotiation into an objective, data-driven competition.

The final pillar, Post-Trade Forensics, is the analytical engine that synthesizes all captured data into a quantifiable assessment of execution quality. This involves a detailed comparison of the executed price against multiple benchmarks:

1. The Pre-Trade Model Price ▴ The primary metric is the spread between the executed level and the independent, model-derived mid-price at the time of the trade. This is often referred to as “slippage” from the theoretical fair value.
2. The Range of Quotes ▴ The executed price is evaluated relative to the full set of quotes received. An execution at or near the best quote received is a strong piece of evidence. The analysis also considers why a quote other than the best might have been chosen (e.g. for reasons of size or counterparty reliability).
3. Historical Analysis ▴ The execution quality is compared against a historical database of similar trades. This helps to identify trends in counterparty performance and to contextualize the cost of the trade relative to past experience under similar market conditions.

Constructing the Internal Benchmark

The combination of these three pillars allows for the creation of a comprehensive execution quality report for each trade. This report is the ultimate output of the strategy, providing a narrative and quantitative justification for the transaction. A key innovation in this area is the development of a composite “Execution Quality Score” (EQS). This score can be a weighted average of several factors, allowing for a single, summary metric that can be tracked over time.

The following table illustrates a simplified structure for such a scoring system:

This strategic approach transforms the abstract requirement of “best execution” into a concrete, measurable, and manageable operational process. It provides a systematic way to navigate the complexities of illiquid markets and to build a powerful, data-driven defense of trading decisions.

Execution

The execution of a best execution quantification framework for illiquid OTC derivatives is an exercise in deep operational and technological integration. It moves from the strategic “what” to the granular “how,” building a robust system for data capture, analysis, and reporting. This system must be embedded within the firm’s daily workflow, functioning as a seamless and indispensable component of the trading and compliance infrastructure.

The Operational Playbook

The implementation of this framework follows a precise, sequential playbook that ensures every trade is supported by a complete and contemporaneous audit trail. This playbook is a series of distinct, mandatory steps that form the backbone of the firm’s execution policy.

1. Pre-Trade Mandate Documentation Before any market approach, the portfolio manager or trader must formally document the trade mandate. This includes the specific instrument characteristics, the target notional amount, the client’s objectives (e.g. hedging a specific risk, expressing a directional view), and any specific constraints or timing requirements. This initial record forms the baseline against which the final execution will be judged.
2. Pre-Trade Analytics And Benchmark Generation The trader then generates a pre-trade analytics report. Using the firm’s internal valuation models or a third-party service, a theoretical mid-market price for the derivative is calculated and time-stamped. This report also includes a snapshot of relevant market data, such as the price of the underlying asset, implied volatility levels, and relevant interest rate curves. This snapshot creates the “reasonableness corridor” for the trade.
3. Counterparty Selection And RFQ Initiation Based on a pre-defined counterparty management policy, the trader selects a list of appropriate dealers to include in the RFQ. The rationale for this selection (e.g. based on historical performance, specialization in the asset class, or risk limits) is recorded. The RFQ is then initiated, either electronically via a platform or through documented communication channels.
4. At-Trade Quote And Order Management All quotes received from counterparties are logged with precise timestamps. This data must be captured systematically. For each quote, the system should automatically calculate the spread to the pre-trade model benchmark. The final execution details ▴ the chosen counterparty, the executed price, the time of execution, and the notional amount ▴ are recorded. If the chosen counterparty did not provide the best price, a justification must be entered by the trader (e.g. “Counterparty B offered a larger size,” or “Counterparty A has lower credit risk”).
5. Post-Trade Analysis And Report Generation Immediately following the execution, the system generates a preliminary Best Execution Report. This report consolidates all the information gathered in the previous steps ▴ the initial mandate, the pre-trade analytics, the full list of quotes received, the executed price, and the calculated slippage against the model price and the best quote. This report is then reviewed and archived by the trading desk and made available to the compliance function.
6. Periodic Review And Framework Optimization On a periodic basis (e.g. quarterly), the compliance or oversight committee reviews the aggregated execution data. This review analyzes trends in execution costs, counterparty performance, and the accuracy of the internal pricing models. The findings from this review are used to refine the counterparty list, improve the valuation models, and update the overall execution policy.

Quantitative Modeling and Data Analysis

The heart of the quantification framework is its quantitative engine. This engine relies on both sophisticated pricing models and a structured approach to data analysis. The goal is to replace subjective judgment with objective, data-driven metrics wherever possible.

A critical component is the proprietary pricing model for the derivative in question. For example, a European-style option on a non-dividend-paying stock might use a Black-Scholes model, while a more complex interest rate swaption would require a more advanced model like the SABR volatility model.

The inputs to these models are as important as the models themselves. The firm must have a reliable, auditable source for all necessary market data. The following table details the typical data inputs required for valuing a common OTC derivative, an interest rate cap:

Once the trade is executed, the post-trade analysis quantifies the outcome using a variety of metrics. The table below shows a sample post-trade report for a hypothetical illiquid equity option trade:

Predictive Scenario Analysis

Consider the case of a mid-sized asset manager, “AlphaGen,” needing to hedge a long position in a portfolio of emerging market corporate bonds. The portfolio manager, Sarah, decides the most efficient way to protect against a widening of credit spreads is to purchase a payer swaption on a relevant credit default swap (CDS) index. This is a highly illiquid, bespoke OTC derivative. The notional is significant ▴ $100 million.

The date is October 26, 2023. The market is moderately volatile due to recent central bank announcements.

Sarah initiates the process by creating a mandate in AlphaGen’s trade management system. The mandate specifies the underlying CDS index, the notional, the strike (the credit spread at which the option is in-the-money), the expiry date, and the objective ▴ “Portfolio hedge against credit spread widening.”

Next, the firm’s quant analyst, Tom, runs the pre-trade valuation model. The model, which uses a stochastic volatility framework calibrated to the current CDS index levels and implied volatilities from the more liquid index options market, generates a theoretical mid-price for the swaption of 85 basis points (bps) of the notional. Tom captures a snapshot of the market ▴ the CDS index is at 250 bps, and the 3-month implied volatility is 22%. He attaches this report to the trade mandate.

Sarah now moves to the counterparty selection stage. AlphaGen’s policy requires a minimum of three quotes for a trade of this size and complexity. She selects four dealers from their approved list ▴ two large, global banks known for their derivatives capabilities (Dealer A and Dealer B), one regional bank with a strong presence in the relevant emerging market (Dealer C), and a specialized derivatives boutique (Dealer D). She initiates an RFQ through their electronic platform, sending the full specifications of the required swaption to all four dealers simultaneously.

The quotes begin to arrive. Dealer A offers at 88 bps. Dealer B comes in at 87 bps. Dealer D, the boutique, offers at 89 bps.

Ten minutes pass, and Dealer C has not responded, which is noted by the system. The best quote is 87 bps from Dealer B. Sarah’s objective is to get the best price, so she prepares to execute with Dealer B. However, before she can, Dealer B’s salesperson calls her. “Sarah, on that swaption, we can do the 87 bps price, but only for $50 million notional. For the full $100 million, we would need to be at 88.5 bps.”

This is a critical piece of information. The electronic quote was not for the full size. Sarah documents this conversation in the trade log. She now faces a choice ▴ execute half the trade at 87 bps and then try to source the rest, potentially at a worse price, or accept a higher price for the full size.

She re-evaluates the other quotes. Dealer A’s offer at 88 bps was for the full $100 million. She quickly calculates that executing the full trade with Dealer A at 88 bps is a better all-in price than splitting the trade. She executes the full $100 million notional with Dealer A at 88 bps.

The system automatically generates the post-trade report. The executed price of 88 bps represents a slippage of +3 bps against the pre-trade model price of 85 bps. The slippage against the best initial quote (Dealer B’s 87 bps) is +1 bp.

However, the report includes Sarah’s note explaining that the best quote was not available for the full size, and that the executed price was the best firm quote for the required notional. The report assigns a high Execution Quality Score, as the process was followed diligently, the decision-making was sound and well-documented, and the outcome was the best available under the circumstances.

This case study demonstrates that the quantification of best execution for an illiquid instrument is not about achieving the theoretical mid-price. It is about the quality and documentation of the entire decision-making process that leads to the final transaction. The framework allowed Sarah to navigate a complex trade, make a defensible decision, and create a complete, auditable record that proves she acted in her client’s best interest.

System Integration and Technological Architecture

A successful best execution framework is underpinned by a seamless technological architecture. This is not a manual, paper-based process. It requires the integration of several core systems to ensure data flows automatically and is captured without error.

• Order Management System (OMS) ▴ The OMS is the system of record for the initial trade mandate. It must have the capability to store the detailed specifications of complex OTC derivatives and to pass this information to the execution and analytics systems.
• Execution Management System (EMS) ▴ For firms that trade electronically, the EMS is the hub for the RFQ process. It must be able to connect to multiple counterparty platforms and to systematically log all quotes received with high-precision timestamps. The EMS should be integrated with the OMS so that executed trades are automatically passed back for allocation and settlement.
• Data Warehouse ▴ A centralized data warehouse is essential for storing all the data related to the best execution process. This includes the pre-trade analytics, the quotes, the execution details, and the post-trade reports. This historical database is the foundation for the periodic review and optimization of the framework.
• Analytics Engine ▴ This can be a proprietary or third-party system that performs the quantitative analysis. It needs to have APIs to pull data from the OMS and the data warehouse, run the valuation models, calculate the slippage metrics, and generate the final reports.
• Connectivity ▴ The entire system relies on robust connectivity to external market data providers (for pricing inputs) and to counterparty systems (for RFQs and execution). This often involves using standard financial messaging protocols like FIX (Financial Information eXchange) for real-time communication.

The goal of the technological architecture is to automate as much of the data capture and analysis as possible. This reduces the operational burden on the trading desk, minimizes the risk of human error, and ensures that the best execution process is applied consistently to every single trade, creating a truly robust and defensible quantification system.

Reflection

From Defensive Record to Offensive Intelligence

The construction of a framework to quantify best execution for illiquid derivatives, while born from a need for regulatory defense, creates a powerful strategic asset. The rigorous collection and analysis of execution data does more than simply build an audit trail; it builds an intelligence engine. Every trade, every quote, every interaction with a counterparty becomes a data point in a proprietary map of the firm’s specific liquidity landscape.

Viewing this system solely through the lens of compliance is to miss its true potential. The data gathered can be used to refine trading strategies, to optimize counterparty selection, and to gain a deeper understanding of the true costs of transacting in opaque markets. Which dealers are consistently competitive in which products? How do execution costs vary with market volatility?

At what trade size does information leakage become a significant factor? The answers to these questions, contained within the firm’s own execution data, are a source of a durable competitive edge.

The ultimate evolution of this framework is to move from a post-trade forensic tool to a pre-trade predictive one. By analyzing historical data, the system can begin to provide intelligent recommendations to the trader before the RFQ is even initiated, suggesting the optimal number of counterparties to approach or the expected cost of execution given the current market state. This transforms the process from a reactive justification of past decisions to a proactive optimization of future ones. The system becomes a partner in the trading process, a source of data-driven insight that enhances, rather than just monitors, the trader’s own judgment and expertise.