How Should an RFQ Strategy Adapt for Illiquid Assets versus Liquid Ones with SIs? ▴ Question

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Concept

An institution’s Request for Quote (RFQ) protocol functions as a precision instrument for sourcing liquidity. Its operational effectiveness is defined by its adaptability to the underlying asset’s market structure. The core challenge an execution desk faces is calibrating this instrument for two fundamentally different environments ▴ the deep, high-velocity markets of liquid assets and the shallow, opaque venues of illiquid ones.

The protocol itself, a standardized message requesting a price for a specified quantity, remains constant. The intelligence lies in its deployment.

For liquid instruments, such as on-the-run government bonds or large-cap equities, the RFQ operates within a known universe of abundant liquidity. Here, the primary objective is competitive price discovery. The system is architected to solicit multiple, simultaneous quotes from a wide panel of dealers and Systematic Internalisers (SIs). SIs, in this context, act as internalized, principal-facing liquidity pools, offering quotes from their own books.

The strategy is predicated on speed and competition, creating a real-time auction that minimizes slippage against the arrival price and achieves incremental price improvement. The information signal sent by the RFQ is low-risk; in a sea of liquidity, a single request is unlikely to perturb the market.

A well-designed RFQ system for liquid assets functions as a competitive auction, optimizing for price improvement and speed.

Conversely, the landscape for illiquid assets, like distressed debt, esoteric derivatives, or off-the-run corporate bonds, presents a starkly different set of problems. The market is characterized by information asymmetry and scarcity of available inventory. Launching a broad, multi-dealer RFQ in this environment is a critical error. Such an action broadcasts a significant information signal across a narrow market, alerting participants to a large, directional interest.

This information leakage almost guarantees adverse price movement, as potential counterparties adjust their offers preemptively. The primary objective shifts from price competition to certainty of execution and impact mitigation.

In this scenario, the RFQ protocol must be reconfigured from a broadcast mechanism to a series of targeted, discreet inquiries. The role of a Systematic Internaliser or a trusted dealer transforms from a competitive bidder to a strategic partner. The relationship is built on a history of successful execution and a deep understanding of the dealer’s specific inventory and risk appetite.

The process becomes sequential and highly curated, prioritizing the protection of information over the speed of polling. The adaptation is therefore a function of risk management, where the definition of “best execution” evolves from the best price to the highest probability of a quiet, stable fill.

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Strategy

Developing a sophisticated RFQ strategy requires a bifurcated approach, architecting two distinct protocols governed by the liquidity profile of the target asset. These frameworks are the Competitive Pricing Protocol for liquid assets and the Targeted Discovery Protocol for illiquid positions. The selection of a strategy is a deliberate choice based on whether the primary execution risk is slippage against a visible market price or the information leakage inherent in sourcing scarce liquidity.

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The Competitive Pricing Protocol for Liquid Assets

For highly liquid instruments, the strategic imperative is to minimize implicit costs by fostering a competitive bidding environment. The RFQ is a tool to harvest price improvement from multiple sources simultaneously. Systematic Internalisers are critical components of this architecture, providing consistent, reliable quotes that can be benchmarked against the broader dealer community and the lit market’s National Best Bid and Offer (NBBO).

The strategy involves configuring the Execution Management System (EMS) to broadcast the RFQ to a pre-defined list of counterparties. This list should be dynamic and data-driven, curated based on historical performance metrics.

Counterparty Tiering ▴ Dealers and SIs are tiered based on metrics such as response time, quote competitiveness, and fill rates. High-tier counterparties receive the majority of the flow.
Automated Sweeping ▴ The EMS is programmed to automatically sweep the top of book across all responding dealers, aggregating liquidity to fill the order at the best possible blended price.
Wave RFQs ▴ For very large orders, the execution can be broken into smaller “waves” to test the market’s depth without revealing the full order size at once. Each wave is a competitive RFQ, allowing the trader to gauge market response and adjust accordingly.

In liquid markets, the RFQ strategy is an exercise in engineering competition to produce measurable price improvement.

The table below outlines the expected outcomes when adapting the counterparty list for a liquid asset RFQ.

Number of Counterparties	Average Response Time (ms)	Price Improvement (bps)	Fill Probability	Primary Outcome
3-5	<100 ms	0.25 bps	98%	High speed, moderate price discovery
6-10	~150 ms	0.40 bps	99%	Optimal balance of competition and speed
11+	>200 ms	0.45 bps	99%	Diminishing returns on price improvement

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How Does Counterparty Selection Define RFQ Success?

The success of any RFQ is determined before the request is even sent; it lies in the meticulous curation of the counterparty list. For illiquid assets, this selection process transcends simple performance metrics and becomes a qualitative assessment of trust and specialization. The Targeted Discovery Protocol is a strategy of surgical precision, designed to uncover liquidity while leaving the faintest possible footprint on the market.

This protocol abandons the concept of a simultaneous broadcast. Instead, it employs a sequential or “staggered” inquiry model. The trader, supported by quantitative analysis, identifies a small number of counterparties, including SIs known to specialize in the specific asset class or risk profile. The process is deliberate and often manual.

Initial Inquiry ▴ The first RFQ is sent to the highest-conviction counterparty. This may be a dealer who has shown the axe (a strong interest) in that security or an SI with a known strong balance sheet for that type of risk.
Information Control ▴ The trader waits for a response before approaching another dealer. This prevents multiple parties from working the same scarce liquidity, a situation that creates a false impression of demand and drives prices away from the initiator.
Relationship Leverage ▴ The communication may extend beyond the electronic protocol, involving voice communication to add context to the request. This reinforces the relationship-based nature of illiquid trading. A trusted SI, in this capacity, acts less like a simple counterparty and more like an extension of the trading desk, discreetly sourcing liquidity on behalf of the client.

The core of this strategy is the preservation of information value. Each RFQ for an illiquid asset is a costly signal. The Targeted Discovery Protocol is designed to expend that signal with maximum efficiency, ensuring that when a trade is finally executed, its price reflects the intrinsic value of the asset, not the market impact of a clumsy search.

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Execution

The execution of an RFQ strategy is where the architectural design meets the realities of the market. The operational workflows for liquid and illiquid assets are fundamentally divergent, requiring different technological configurations, risk controls, and performance benchmarks. A failure to distinguish between these execution architectures exposes the firm to unnecessary costs and execution risk.

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Execution Workflow Architectures

The practical implementation of an RFQ is managed through an Execution Management System (EMS) or an Order Management System (OMS). The configuration of this system must be flexible enough to handle both the high-throughput, automated nature of liquid RFQs and the cautious, manual process required for illiquids.

Effective execution is the result of a pre-configured workflow that aligns the firm’s technology with the specific liquidity characteristics of the asset.

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What Are the Primary Execution Risks in Each Scenario?

The risks in each workflow are distinct. For liquid assets, the primary risk is operational and technological ▴ system latency or a failure to process a large number of quotes efficiently can lead to missed opportunities and negative slippage. For illiquid assets, the risk is entirely strategic ▴ poor counterparty selection or premature information disclosure can poison the market for the asset, making a successful execution at a fair price impossible.

The following tables detail the discrete steps and parameters for each protocol.

Table ▴ Liquid Asset RFQ Execution Protocol

Step	Action	System	Key Parameter	Rationale
1. Pre-Trade	Define order parameters and select pre-approved counterparty list (10+ dealers/SIs).	OMS/EMS	Order Size, Limit Price	Set baseline execution goals and constraints.
2. Initiation	Initiate simultaneous RFQ broadcast to all selected counterparties.	EMS	Time-in-force (e.g. 15 seconds)	Create a competitive, time-bound auction environment.
3. Aggregation	System automatically aggregates all incoming quotes in real-time.	EMS	Live NBBO	Benchmark incoming prices against the lit market.
4. Execution	Automated execution against the best responding price(s) or manual execution by trader.	EMS	Price Improvement	Capture the economic benefit of the competitive process.
5. Post-Trade	Allocate fills and send to Transaction Cost Analysis (TCA) system.	OMS/TCA	Slippage vs. Arrival	Measure execution quality and refine counterparty lists.

Table ▴ Illiquid Asset RFQ Execution Protocol

Step	Action	System	Key Parameter	Rationale
1. Pre-Trade	Identify 2-3 trusted, specialist counterparties based on historical data and qualitative insight.	Trader Analysis	Counterparty Rank	Prioritize dealers most likely to provide a discreet, fair market.
2. Initiation	Send single RFQ to top-ranked counterparty. May supplement with voice call.	EMS/Voice	Quote Validity (minutes/hours)	Allow counterparty time to source liquidity without market pressure.
3. Evaluation	Trader evaluates the quote based on internal valuation models.	Trader Analysis	Internal NAV/Fair Value	Assess the price against fundamental value, not just market activity.
4. Decision	If quote is unacceptable, wait and approach second-ranked counterparty. Do not run parallel inquiries.	Trader Discretion	Information Leakage	Strictly control the information signal to prevent market impact.
5. Post-Trade	Allocate fill and document execution rationale, focusing on process over pure price.	OMS/Compliance	Implementation Shortfall	Measure the full cost of execution, including delay and opportunity cost.

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Quantitative Metrics and System Integration

The measurement of success differs as dramatically as the process itself. For liquid RFQs, TCA focuses on micro-level price metrics. For illiquid RFQs, the analysis must capture the broader context of the trade.

Liquid Metrics ▴ The key metric is price improvement versus the arrival price (the market price at the moment the order was initiated). The entire system, from co-located servers to optimized network routes, is designed to minimize latency and maximize the chance of capturing a fleeting, improved price. Integration via the FIX protocol relies on standard messages like QuoteRequest (Tag 35=R) and QuoteResponse (Tag 35=AJ) for high-speed communication.
Illiquid Metrics ▴ The benchmark shifts to implementation shortfall. This metric compares the final execution price to the decision price (the price when the original investment decision was made). It captures not only the explicit cost of the trade but also the implicit costs of delay and market impact. The execution system must allow for detailed note-taking and rationale capture, as the justification for the trade is a core part of the compliance and best execution record. System integration is less about speed and more about providing the trader with access to valuation models, historical data, and secure communication channels.

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References

Diderich, Claude. “Managing illiquidity in a liquid way.” innovate.d, 2019.
Goy, Samuel. “The Challenge of Asset Allocation with Illiquid Private Investments.” SUERF Policy Brief, No. 557, March 2023.
Financial Conduct Authority. “Discussion Paper DP17/1 ▴ Illiquid assets and open-ended investment funds.” FCA, February 2017.
Harris, Larry. Trading and Exchanges ▴ Market Microstructure for Practitioners. Oxford University Press, 2003.
O’Hara, Maureen. Market Microstructure Theory. Blackwell Publishers, 1995.
Lehalle, Charles-Albert, and Sophie Laruelle, editors. Market Microstructure in Practice. World Scientific Publishing, 2013.
Foucault, Thierry, et al. “Optimal liquidity provision.” The Review of Financial Studies, vol. 26, no. 4, 2013, pp. 875-918.

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Reflection

An institution’s execution architecture is a reflection of its market philosophy. The protocols and systems in place are the tangible manifestations of how that firm understands risk, values information, and defines success. The treatment of the RFQ protocol provides a particularly clear diagnostic.

Does your framework treat this protocol as a monolithic, one-size-fits-all tool, or as an adaptive instrument sensitive to the environment? Where are the points of friction in your workflow when a portfolio manager’s directive shifts from a liquid, high-turnover strategy to a long-term, illiquid position? The answers to these questions reveal the true sophistication of an operational system. The knowledge of how to adapt is one component; building an architecture that makes this adaptation seamless and repeatable is the foundation of a durable competitive edge.