How Should a TCA Framework for Illiquid RFQs Be Adjusted for Different Asset Classes like Bonds and Swaps?

Concept

An institutional TCA framework built for liquid, order-driven equity markets is fundamentally unsuitable for the architecture of illiquid RFQ markets. Applying its logic directly to instruments like bespoke corporate bonds or structured interest rate swaps creates analytical friction and misleading performance signals. The challenge originates in the very nature of price discovery. In an equity market, a continuous stream of public data provides a persistent, observable benchmark.

The RFQ protocol, conversely, operates within a discontinuous, private environment. It is a mechanism designed to create a temporary, competitive market for a specific, often unique, instrument at a single point in time. Therefore, adjusting a TCA framework requires a complete reframing of the objective. The goal shifts from measuring an execution against a continuous public price stream to evaluating the quality and efficiency of the discrete price discovery process itself.

The core function of a TCA system in this context is to provide a structured, data-driven answer to a series of critical operational questions. How effective was the counterparty selection process? Was the full depth of available liquidity accessed? What was the information leakage cost associated with signaling trading intent to a select group of dealers?

For a specific, off-the-run corporate bond, the concept of a Volume-Weighted Average Price (VWAP) is meaningless; there is no continuous volume against which to weigh the price. The true benchmark is the competitive tension generated among the responding dealers, measured against a reliable, independent valuation at the moment of execution. The system must capture the entire lifecycle of the RFQ, from the initial dealer selection to the final fill, treating each data point not as a component of a statistical average, but as a piece of evidence in an audit of process integrity.

A TCA framework for illiquid RFQs must evolve from a tool of price comparison into a system for auditing the quality of the price discovery process.

This adjusted perspective treats the RFQ workflow as a system to be optimized. For bonds, the system must account for the high degree of instrument fragmentation where each CUSIP is a market of one. For swaps, the system must parse the multi-dimensional nature of the instrument, where the final price is a function of tenor, underlying rates, collateral agreements, and counterparty creditworthiness. In both cases, the TCA framework becomes the primary tool for quantifying the effectiveness of the firm’s liquidity sourcing strategy.

It provides the data necessary to refine dealer lists, optimize the number of dealers queried for a given instrument type, and understand the implicit costs of information signaling in fragmented, dealer-centric markets. The framework’s value is realized through its ability to impose structure and transparency upon an inherently opaque process, providing a quantitative basis for improving execution outcomes in markets where every basis point of cost is magnified by trade size and infrequency.

Strategy

Developing a TCA strategy for illiquid RFQs demands a bespoke approach for each asset class, moving beyond generic metrics to capture the unique drivers of transaction costs in bond and swap markets. The strategic objective is to create a data architecture that can deconstruct an execution into its core cost components, attributing each to specific decisions made during the trading workflow. This requires a granular understanding of how value is defined and negotiated in each market.

Differentiating the Approach for Bonds

For corporate and municipal bonds, the primary challenge is instrument heterogeneity and fragmented liquidity. A TCA strategy must pivot from price-based benchmarks to process- and quote-based benchmarks. The system must be architected to measure the quality of the competitive environment created by the RFQ.

The key strategic elements include:

• Peer Group Benchmarking The most effective benchmark for an illiquid bond is often a composite price derived from multiple sources or a peer universe comparison. This provides an independent, market-vetted reference point that is more resilient than a single dealer’s mark. Platforms that aggregate executed trade data provide a powerful tool for this, allowing a firm to see its execution quality in the context of the broader market’s activity on a given day.
• Quote Funnel Analysis The strategy involves analyzing the entire set of quotes received, not just the winning one. Key metrics include the number of dealers invited versus the number who responded, the time-to-respond for each dealer, and the statistical distribution of the quotes. A wide distribution may indicate high uncertainty or low competition, while a tight distribution suggests a more consensus-driven price.
• Information Leakage Measurement A sophisticated strategy attempts to quantify the cost of signaling. This can be achieved by comparing the execution price to a pre-trade benchmark captured the instant before the RFQ is sent. Any adverse movement in that benchmark during the quoting window, especially if correlated with the RFQ, can be a proxy for market impact.

The following table illustrates the strategic shift in TCA metrics when moving from a liquid, exchange-traded asset to an illiquid, RFQ-driven bond.

How Should a TCA Strategy Adapt for Swaps?

Swaps introduce another layer of complexity because they are multi-dimensional contracts, and their value is intrinsically linked to counterparty risk. A TCA strategy for swaps must normalize for these factors to allow for meaningful comparison.

The strategic differentiation for swaps lies in normalizing execution costs against risk factors like DV01 and incorporating the economic impact of counterparty selection.

The architecture for swap TCA is built upon these principles:

• Risk-Normalized Cost Analysis The cost of a swap trade should be measured in terms of its primary risk sensitivity. For an interest rate swap (IRS), this means calculating the cost in terms of basis points per unit of DV01 (the dollar value of a one basis point change in rates). This normalizes the analysis, allowing for the comparison of a 5-year swap with a 30-year swap on a risk-equivalent basis. For a credit default swap (CDS), the equivalent would be cost per CS01.
• Holistic Quote Evaluation The “price” of a swap is more than just the fixed rate. The TCA system must capture and evaluate variations in collateral agreements, coupon frequencies, and other non-standard terms that have a direct economic impact. A seemingly better rate from one counterparty might be attached to less favorable collateral terms, making it a more expensive trade over its lifecycle.
• Counterparty Cost Analysis In bilateral (non-cleared) swaps, the choice of counterparty is a primary driver of cost. The TCA framework must integrate metrics that reflect this, such as the cost of funding and the credit valuation adjustment (CVA) associated with each dealer. A dealer with a lower credit rating may offer a better price upfront, but this is offset by the higher implicit cost of counterparty risk. For cleared swaps, the analysis shifts to the costs imposed by the central counterparty (CCP).

By designing distinct strategies for bonds and swaps, an institution can build a TCA framework that provides actionable intelligence. It moves the conversation from a simplistic “Did we get a good price?” to a more sophisticated “Did our process yield the optimal outcome given the market structure and instrument characteristics?”

Execution

The execution of a TCA framework for illiquid RFQs is an exercise in data architecture and process engineering. It requires the systematic capture, normalization, and analysis of data points that are often ephemeral in traditional voice-driven workflows. The objective is to construct a complete, auditable record of the trading decision, transforming the RFQ process from a series of conversations into a structured dataset.

The Operational Playbook

Implementing a robust TCA program for illiquid instruments follows a clear, multi-stage procedure. This playbook ensures that all relevant data is captured at the correct point in the trade lifecycle, forming the foundation for all subsequent analysis.

1. Pre-Trade Snapshot The process begins the moment the decision to trade is made. The system must automatically capture a comprehensive snapshot of the market environment. This includes benchmark rates, relevant credit spreads, and, most importantly, an independent, time-stamped evaluated price for the specific bond or a model-derived price for the proposed swap. This pre-trade mark serves as the primary “arrival price” benchmark against which all subsequent costs are measured.
2. RFQ Event Logging Every action within the RFQ workflow must be logged with millisecond precision. This includes the exact time the RFQ is sent to each dealer, the time each dealer’s response is received, and the time of any modifications or withdrawals. The identity of each dealer, the specifics of their quote (price, size, and any qualifying terms), and the final execution details must be linked to a single parent trade identifier. Loading data from voice trades via API is a critical capability to ensure a complete view of all activity.
3. Quote Stack Decomposition Upon completion of the RFQ, the system must analyze the entire set of received quotes. This goes far beyond simply noting the winning bid. The analysis should compute the mean, median, and standard deviation of the quote set. The difference between the winning quote and the next best quote (the “winner’s curse” component) should be calculated, as should the spread between the best bid and best offer from the pool of respondents.
4. Multi-Benchmark Analysis The final execution price is compared against a series of benchmarks to deconstruct the total transaction cost. This includes:
   • The pre-trade snapshot price (measuring market impact/slippage).
   • The best quote received (measuring how much of the final spread was captured).
   • The median quote received (measuring execution quality relative to the consensus).
   • A post-trade snapshot price (e.g. 5-15 minutes after execution) to assess information leakage and potential market reversion.

Quantitative Modeling and Data Analysis

What are the precise data fields needed for this analysis? The data architecture must be tailored to the specific attributes of each asset class. While both require a core set of RFQ lifecycle data, the instrument-specific fields are what enable a truly meaningful, comparative analysis. The absence of reliable transaction timestamps and initiator data in many OTC markets necessitates a focus on creating these data points internally through disciplined operational logging.

A successful execution framework is defined by its ability to capture and normalize the unique economic drivers of each asset class within a unified data model.

The following table provides a granular view of the required data architecture, highlighting the distinct fields necessary for a comparative TCA between illiquid bonds and interest rate swaps.

This structured data allows for the creation of powerful summary metrics. For instance, “Implementation Shortfall” for an RFQ can be calculated as (Execution Price – Pre-Trade Benchmark Price). This can then be broken down into (Execution Price – Best Quote Price) representing the portion of the bid-offer spread paid, and (Best Quote Price – Pre-Trade Benchmark Price) representing the market impact or information leakage during the quoting window.

For swaps, all these cost components would be divided by the trade’s DV01 to arrive at a normalized, comparable metric. This quantitative rigor transforms TCA from a regulatory compliance exercise into a powerful tool for systematic performance improvement.

Reflection

The architecture of an effective TCA system for illiquid instruments ultimately reflects the sophistication of a firm’s own operational philosophy. The data it generates is more than a record of past performance; it is a blueprint for future strategy. By quantifying the nuances of dealer engagement, information signaling, and risk-adjusted cost, the framework provides a persistent feedback loop for improvement.

The insights derived from this system should permeate beyond the trading desk, informing portfolio management decisions and shaping the firm’s strategic approach to liquidity sourcing. The true value of this analytical machinery lies in its ability to transform opaque, relationship-driven markets into a domain of quantitative decision-making, providing a durable edge in capital allocation and execution.