# How Does an RFQ Protocol Alter the Pricing Strategy of a Market Maker? ▴ Question

**Published:** 2025-07-31
**Categories:** Question

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![Geometric planes and transparent spheres represent complex market microstructure. A central luminous core signifies efficient price discovery and atomic settlement via RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-microstructure-4.jpg)

![Smooth, layered surfaces represent a Prime RFQ Protocol architecture for Institutional Digital Asset Derivatives. They symbolize integrated Liquidity Pool aggregation and optimized Market Microstructure](https://options.greeks.live/wp-content/uploads/2025/07/market-microstructure-rfq-protocols-for-digital-asset-derivatives.jpg)

## Concept

The introduction of a [Request for Quote](https://prime.greeks.live/area/request-for-quote/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (RFQ) protocol into a market maker’s workflow is a systemic redesign of its core function. It represents a fundamental shift in the informational and relational dynamics that govern price formation. A market maker operating on a [central limit order book](https://prime.greeks.live/area/central-limit-order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (CLOB) is a passive respondent to anonymous market flow, setting bids and offers based on public data, volatility forecasts, and inventory levels. The firm’s strategy is one of mass-market price provision, engineered for high-frequency, low-latency reaction to a broadcast of public information.

Engaging with an [RFQ protocol](https://prime.greeks.live/area/rfq-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) dismantles this paradigm. The [market maker](https://prime.greeks.live/area/market-maker/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is no longer a passive recipient of anonymous orders. It becomes an active participant in a discrete, bilateral negotiation, even if that negotiation is automated and lasts only milliseconds. The request from a client is a targeted signal, a private piece of information that arrives outside the public feed.

This signal contains data far richer than a simple market order. It contains the identity of the counterparty, the precise size of the desired transaction, and an implicit urgency. The [pricing strategy](https://prime.greeks.live/area/pricing-strategy/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), therefore, transforms from a generalized, statistical exercise into a bespoke, counterparty-aware calculation of risk and opportunity. It is a transition from broadcasting a price to the world to whispering a price to a specific client.

> A request-for-quote system reconfigures a market maker’s pricing engine from a public utility into a private intelligence operation.

This alteration compels a complete re-evaluation of the firm’s operational architecture. The pricing model must evolve to incorporate new variables that were previously abstracted away or deemed irrelevant in the anonymous churn of the CLOB. The identity of the requesting client becomes a primary input. A market maker must begin to quantify the informational content of a client’s flow.

Is this client’s trading activity typically informed or uninformed? Does their past behavior suggest a pattern of [adverse selection](https://prime.greeks.live/area/adverse-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), where they possess short-term private information that leaves the market maker holding a position at a loss, a phenomenon known as the ‘winner’s curse’? Answering these questions requires a robust data infrastructure and analytical capabilities that go far beyond simple time-series analysis of market prices.

The pricing strategy becomes an exercise in game theory. The market maker must model the client’s intentions and the potential actions of competing market makers who may also be responding to the same RFQ. The price quoted is a strategic move in this game. A price that is too aggressive may win the trade but result in a loss if the client is trading on superior information.

A price that is too conservative protects the market maker but sacrifices volume and market share. The optimal price is one that balances this trade-off, maximizing expected profit over a series of interactions with a specific client and across the broader market. This requires a dynamic pricing engine that can adjust its parameters in real time based on a continuous stream of data about client behavior, market conditions, and the firm’s own risk appetite and inventory position.

![A precision-engineered metallic institutional trading platform, bisected by an execution pathway, features a central blue RFQ protocol engine. This Crypto Derivatives OS core facilitates high-fidelity execution, optimal price discovery, and multi-leg spread trading, reflecting advanced market microstructure](https://options.greeks.live/wp-content/uploads/2025/08/precision-engineered-institutional-digital-asset-derivatives-execution-core-facilitating-rfq-protocol-driven-optimal-price-discovery-and-multi-leg-spread-trading-for-capital-efficiency.jpg)

![A bifurcated sphere, symbolizing institutional digital asset derivatives, reveals a luminous turquoise core. This signifies a secure RFQ protocol for high-fidelity execution and private quotation](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-orchestration.jpg)

## Strategy

The strategic recalibration for a market maker integrating RFQ protocols is a multi-layered process. It moves the firm’s competitive ground from pure latency and model speed to the more subtle domains of counterparty intelligence, tailored risk management, and inventory optimization. The core objective shifts from capturing the [bid-ask spread](https://prime.greeks.live/area/bid-ask-spread/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) on a massive volume of anonymous trades to pricing a smaller number of discrete, information-rich trades with precision and foresight. This demands a new strategic framework built on three pillars ▴ Counterparty Tiering, [Dynamic Spread](https://prime.greeks.live/area/dynamic-spread/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Calibration, and Inventory-Driven Skewing.

![Abstract representation of a central RFQ hub facilitating high-fidelity execution of institutional digital asset derivatives. Two aggregated inquiries or block trades traverse the liquidity aggregation engine, signifying price discovery and atomic settlement within a prime brokerage framework](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-hub-core.jpg)

## Counterparty Intelligence and Tiering

In a CLOB environment, all counterparties are treated as equal and anonymous. The RFQ protocol shatters this anonymity, making counterparty identification the cornerstone of pricing strategy. A market maker must develop a system for classifying clients into tiers based on the informational content of their past trading activity. This is a quantitative process that analyzes historical trade data to identify patterns of adverse selection.

- 
**Tier 1 Clients (Uninformed Flow)** ▴ These are typically large asset managers, pension funds, or corporate hedgers whose trading activity is driven by portfolio rebalancing, hedging needs, or long-term investment theses. Their trades are not usually motivated by short-term private information about the asset’s future price. A market maker can offer tighter spreads to this client segment, as the risk of being adversely selected is low. The strategy is to win a high percentage of this flow to generate consistent, low-risk revenue.

- 
**Tier 2 Clients (Mixed Flow)** ▴ This category includes smaller hedge funds or algorithmic traders whose flow may be informed at times but is generally mixed with liquidity-seeking trades. The pricing strategy for this tier is more dynamic. Spreads will be wider than for Tier 1 clients and may adjust based on real-time market conditions and the specific characteristics of the RFQ.

- 
**Tier 3 Clients (Informed Flow)** ▴ This segment consists of counterparties, often sophisticated quantitative funds or proprietary trading firms, whose trading activity has historically demonstrated a high degree of short-term predictive power. When these clients request a quote, there is a high probability they possess information that the market maker does not. The pricing strategy here is primarily defensive. Spreads will be significantly wider, and the market maker may choose not to quote at all during periods of high uncertainty. The goal is to avoid significant losses from the winner’s curse.

![A sleek, pointed object, merging light and dark modular components, embodies advanced market microstructure for digital asset derivatives. Its precise form represents high-fidelity execution, price discovery via RFQ protocols, emphasizing capital efficiency, institutional grade alpha generation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-digital-asset-rfq-execution-system-microstructure.jpg)

## What Is the Core Function of Dynamic Spread Calibration?

The spread quoted in an RFQ is not a static value. It is a dynamic calculation that must adapt in real time to a range of inputs. The static, one-size-fits-all spread of the CLOB is replaced by a multi-factor model. This model calibrates the bid-ask spread based on the specific context of each request.

The components of this dynamic spread are systematically calculated. The base spread is derived from the prevailing market conditions on the lit markets, including the current bid-ask spread, volatility, and depth of the [order book](https://prime.greeks.live/area/order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). To this base, several adjustment factors are applied. The counterparty tier is a primary adjustment factor; a Tier 3 client will receive a significantly wider spread than a Tier 1 client.

The size of the requested trade is another critical factor. A large [block trade](https://prime.greeks.live/area/block-trade/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) that significantly impacts the market maker’s inventory position will command a wider spread to compensate for the increased risk. The market maker’s [current inventory](https://prime.greeks.live/question/how-does-sa-ccr-improve-upon-the-current-exposure-methodology/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) level also plays a crucial role. If the firm is already long an asset, it will quote a more aggressive (lower) offer price to offload inventory and a less aggressive (lower) bid price to avoid accumulating more. This is known as inventory-driven skewing.

> The shift to RFQ protocols compels a market maker to price the counterparty as much as the instrument itself.

![A sleek, modular institutional grade system with glowing teal conduits represents advanced RFQ protocol pathways. This illustrates high-fidelity execution for digital asset derivatives, facilitating private quotation and efficient liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-ecosystem-powering-high-fidelity-rfq-execution-and-optimal-liquidity-discovery.jpg)

## Inventory Management as a Strategic Tool

In an RFQ world, [inventory management](https://prime.greeks.live/area/inventory-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) transforms from a reactive risk-control function into a proactive alpha-generation strategy. The ability to source liquidity directly from clients allows a market maker to manage its inventory with greater precision. Instead of relying solely on the public markets to offload unwanted positions, a market maker can use the RFQ protocol to find the other side of a trade discreetly.

For example, if a market maker has accumulated a large long position in an asset due to its CLOB market-making activities, it can proactively send out RFQs to a select group of Tier 1 clients who are known to be natural buyers of that asset. This allows the firm to reduce its [inventory risk](https://prime.greeks.live/area/inventory-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) without impacting the [public market](https://prime.greeks.live/question/could-excessive-dark-pool-trading-volume-destabilize-the-primary-public-exchanges/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) price, which would occur if it were to sell a large block on the lit order book. This strategic use of RFQs to manage inventory can significantly reduce hedging costs and improve the overall profitability of the market-making operation.

The table below illustrates the fundamental differences in the strategic components of a market maker’s pricing model when operating on a CLOB versus an RFQ protocol.

### Table 1 ▴ Comparison of Pricing Strategy Components

| Pricing Component | Central Limit Order Book (CLOB) Strategy | Request for Quote (RFQ) Strategy |
| --- | --- | --- |
| Counterparty Analysis | Anonymous. All flow is treated equally. | Counterparty-specific. Tiering based on historical analysis of adverse selection. |
| Spread Determination | Generalized. Based on public market data (volatility, spread, depth). | Bespoke. Dynamically calibrated based on counterparty tier, trade size, and market conditions. |
| Information Source | Public market data feed (Level 2 data). | Private RFQ signal, supplemented by public market data and internal counterparty data. |
| Risk Focus | Inventory risk managed through high-frequency hedging on the CLOB. | Adverse selection risk (winner’s curse) managed through pricing; inventory risk managed through targeted RFQs. |
| Pricing Objective | Maximize capture of the bid-ask spread on high volume of trades. | Maximize expected profit on a smaller volume of information-rich trades, balancing risk and reward. |

![A precision mechanism with a central circular core and a linear element extending to a sharp tip, encased in translucent material. This symbolizes an institutional RFQ protocol's market microstructure, enabling high-fidelity execution and price discovery for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-engineered-institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-facilitating-optimal-price-discovery-and-best-execution.jpg)

![Smooth, reflective, layered abstract shapes on dark background represent institutional digital asset derivatives market microstructure. This depicts RFQ protocols, facilitating liquidity aggregation, high-fidelity execution for multi-leg spreads, price discovery, and Principal's operational framework efficiency](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-multi-leg-spreads.jpg)

## Execution

The execution framework for an RFQ-based pricing strategy is a complex synthesis of quantitative modeling, technological infrastructure, and operational protocols. It represents the tangible implementation of the strategic principles outlined previously. A market maker must build a sophisticated operational playbook that governs how it responds to each RFQ, a robust quantitative engine to generate prices, and a resilient technological architecture to support the entire process. The execution is where the theoretical strategy meets the unforgiving reality of the market.

![An intricate mechanical assembly reveals the market microstructure of an institutional-grade RFQ protocol engine. It visualizes high-fidelity execution for digital asset derivatives block trades, managing counterparty risk and multi-leg spread strategies within a liquidity pool, embodying a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-architecture.jpg)

## The Operational Playbook

The operational playbook is a detailed set of procedures that guide the trading desk in its daily operations. It translates the high-level strategy into a series of concrete, repeatable actions. This playbook is a living document, continuously updated based on performance analysis and changing market dynamics.

- 
**RFQ Ingestion and Triage** ▴ The first step is the automated ingestion of the RFQ, typically via a FIX (Financial Information eXchange) protocol message or a proprietary API. The system immediately parses the request, identifying the client, the instrument, the direction (buy/sell), and the quantity. The client ID is cross-referenced with the internal counterparty tiering database to assign a risk score to the request.

- 
**Pre-Quote Analysis** ▴ Before a price is even calculated, a series of automated checks are performed. The system verifies the market maker’s current inventory position in the asset, checks against pre-defined risk limits (e.g. maximum position size, maximum single-[trade size](https://prime.greeks.live/area/trade-size/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)), and assesses the current state of the lit markets. Is the market in a fast-moving state? Is volatility expanding rapidly? Is the order book thin? If any of these pre-quote checks fail, the system may automatically decline to quote, sending a “No Quote” message back to the client.

- 
**Quantitative Price Generation** ▴ If the pre-quote checks pass, the RFQ is passed to the quantitative pricing engine. This engine is the core of the execution system. It takes in all the relevant data points ▴ the client’s tier, the trade size, the current inventory, the real-time [market data](https://prime.greeks.live/area/market-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) ▴ and feeds them into a pricing model to generate a bespoke bid and offer. This process is detailed further in the next section.

- 
**Quote Dissemination and Monitoring** ▴ The generated quote is sent back to the client. The system then enters a monitoring phase, awaiting the client’s response. The quote is typically valid for a short period, often just a few seconds or even milliseconds. The system must track the status of all outstanding quotes and be prepared to execute immediately if the client accepts.

- 
**Post-Trade Analysis and Model Refinement** ▴ After a trade is executed, the data is fed into a post-trade analysis system. This system calculates the profitability of the trade over various time horizons (e.g. 1 second, 10 seconds, 1 minute) to assess whether the market maker was adversely selected. This data is then used to refine the counterparty tiering system and the pricing models. This continuous feedback loop is essential for the long-term success of the strategy.

![Intersecting teal cylinders and flat bars, centered by a metallic sphere, abstractly depict an institutional RFQ protocol. This engine ensures high-fidelity execution for digital asset derivatives, optimizing market microstructure, atomic settlement, and price discovery across aggregated liquidity pools for Principal Market Makers](https://options.greeks.live/wp-content/uploads/2025/07/optimized-rfq-protocol-for-institutional-digital-asset-derivatives.jpg)

## Quantitative Modeling and Data Analysis

The heart of the execution framework is the quantitative model that generates the price. This model is a departure from the simpler models used for CLOB market making. It must explicitly account for the risk of adverse selection.

A common approach is to model the ‘fair value’ of the asset and then apply a carefully calibrated spread around that value. The spread itself is a function of multiple variables.

The price (P) quoted to a client can be represented by a formula such as:

_P_bid = E – (Spread_base + Spread_adverse_selection + Spread_inventory_risk)_

_P_offer = E + (Spread_base + Spread_adverse_selection + Spread_inventory_risk)_

Where:

- 
**E ** is the expected future value of the asset over a short time horizon (the ‘fair value’). This is typically derived from the midpoint of the lit market’s bid-ask spread, potentially adjusted for micro-price imbalances.

- 
**Spread_base** is a function of market volatility and liquidity on the CLOB.

- 
**Spread_adverse_selection** is a function of the client’s tier and the trade size. This is the component that widens the spread for clients who are more likely to be informed.

- 
**Spread_inventory_risk** is a function of the market maker’s current inventory and the size of the trade. This component adjusts the price to incentivize trades that reduce the firm’s risk.

The table below provides a granular example of how these components might be calculated for a specific RFQ.

### Table 2 ▴ Granular RFQ Pricing Model Calculation

| Parameter | Input Value | Calculation Step | Result (in USD) |
| --- | --- | --- | --- |
| RFQ Details | Client ▴ Fund XYZ, Buy 100 BTC | – | – |
| Market Midpoint (E ) | 60,000 USD | Base reference price | 60,000.00 |
| Base Spread | Volatility ▴ 2%, Liquidity Score ▴ 0.8 | 0.01% (Volatility / Liquidity) | 15.00 |
| Adverse Selection Spread | Client Tier ▴ 2, Trade Size ▴ 100 BTC | (Tier Multiplier 0.005%) (Size Multiplier 0.01%) | 25.00 |
| Inventory Risk Spread | Current Inventory ▴ +200 BTC, Trade Size ▴ +100 BTC | (Inventory Skew Factor 0.02%) | 30.00 |
| Total Spread | – | Sum of all spread components | 70.00 |
| Final Quoted Offer Price | – | E + Total Spread | 60,070.00 |

![Modular, metallic components interconnected by glowing green channels represent a robust Principal's operational framework for institutional digital asset derivatives. This signifies active low-latency data flow, critical for high-fidelity execution and atomic settlement via RFQ protocols across diverse liquidity pools, ensuring optimal price discovery](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution.jpg)

## How Does System Integration Impact Pricing Strategy?

The execution of an RFQ pricing strategy is critically dependent on the seamless integration of various technology systems. The entire process, from receiving the RFQ to post-trade analysis, must be automated and operate at very low latencies. The technological architecture is the backbone that supports the entire operation.

The Financial Information eXchange (FIX) protocol is the industry standard for electronic communication in financial markets. RFQ workflows are typically managed using specific FIX message types. For example:

- 
**QuoteRequest (FIX Tag 35=R)** ▴ This message is sent by the client to the market maker to request a quote. It contains the instrument, side, and quantity.

- 
**Quote (FIX Tag 35=S)** ▴ This is the market maker’s response, containing the bid price, offer price, and the quantities for which those prices are firm.

- 
**ExecutionReport (FIX Tag 35=8)** ▴ If the client accepts the quote, they send an order that results in an execution report confirming the trade.

The market maker’s system architecture must include a FIX engine capable of handling these messages with minimal latency. This engine is connected to the firm’s Order Management System (OMS) and Execution Management System (EMS). The OMS tracks the firm’s overall positions and risk, while the EMS houses the smart order routing logic and the pricing engines. The counterparty database, the real-time market data feed, and the quantitative pricing models are all integrated into this central system.

A high-speed messaging bus is used to pass information between these components in real time. The entire architecture is designed for speed, resilience, and scalability.

> A market maker’s RFQ price is the output of a complex system where counterparty data, market signals, and inventory risk are processed through a high-speed technological chassis.

![A glowing blue module with a metallic core and extending probe is set into a pristine white surface. This symbolizes an active institutional RFQ protocol, enabling precise price discovery and high-fidelity execution for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-driving-institutional-digital-asset-derivatives-high-fidelity-execution-precise-price-discovery-for-block-trades-and-capital-efficiency.jpg)

## Predictive Scenario Analysis

Consider a scenario where a market maker, MM-Alpha, receives an RFQ to sell 1,500 ETH from a client, HF-Quant, who is classified as a Tier 3 (informed) counterparty. The time is 14:30:00 UTC. The lit market for ETH/USD is currently 4,000.00 / 4,000.50. MM-Alpha’s pricing system immediately goes to work.

The fair value E is calculated as 4,000.25. The base spread, given current low volatility, is calculated at $0.25. However, the system’s counterparty model flags HF-Quant as a high-risk client. The adverse selection model, trained on thousands of past trades, assigns a high probability that HF-Quant possesses negative information about ETH’s price in the near future.

It adds an [adverse selection spread](https://prime.greeks.live/question/how-does-dealer-selection-influence-the-severity-of-adverse-selection-in-illiquid-markets/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) of $2.50 to the quote. MM-Alpha’s current inventory is flat, so the inventory risk spread is minimal, at $0.10. The total spread is $2.85. Therefore, MM-Alpha’s system quotes a bid price of 3,997.40 (4,000.25 – 2.85).

Simultaneously, a competing market maker, MM-Beta, who has a less sophisticated counterparty model, classifies HF-Quant as a Tier 2 client. Their system calculates a smaller adverse selection spread of $1.00 and quotes a more aggressive bid of 3,999.15. HF-Quant, as expected, hits MM-Beta’s bid and sells 1,500 ETH at 3,999.15. Ten seconds later, a large sell order hits the public market, and the price of ETH drops to 3,995.00.

MM-Beta is now holding 1,500 ETH at a mark-to-market loss of over $6,000. MM-Alpha, by quoting a wider, more defensive price, avoided the trade and the subsequent loss. This scenario illustrates the critical importance of a sophisticated, data-driven execution framework in the RFQ space. The ability to accurately price the risk of adverse selection is the key to survival and profitability.

![A sleek, segmented capsule, slightly ajar, embodies a secure RFQ protocol for institutional digital asset derivatives. It facilitates private quotation and high-fidelity execution of multi-leg spreads a blurred blue sphere signifies dynamic price discovery and atomic settlement within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-3.jpg)

## References

- Biais, Bruno, Dominique Foucault, and Sophie Moinas. “Equilibrium Discovery and Preopening Periods in Financial Markets.” _The Journal of Finance_, vol. 70, no. 4, 2015, pp. 1583-1629.

- Bessembinder, Hendrik, and Kumar Venkataraman. “Does an Electronic Stock Exchange Need an Upstairs Market?” _Journal of Financial Economics_, vol. 73, no. 1, 2004, pp. 3-36.

- Grossman, Sanford J. and Merton H. Miller. “Liquidity and Market Structure.” _The Journal of Finance_, vol. 43, no. 3, 1988, pp. 617-33.

- Harris, Larry. _Trading and Exchanges ▴ Market Microstructure for Practitioners_. Oxford University Press, 2003.

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

- Hendershott, Terrence, and Ryan Riordan. “Algorithmic Trading and the Market for Liquidity.” _Journal of Financial and Quantitative Analysis_, vol. 48, no. 4, 2013, pp. 1001-24.

- Foucault, Thierry, Ohad Kadan, and Eugene Kandel. “Liquidity, Information, and Infrequent Trading.” _The Journal of Finance_, vol. 68, no. 4, 2013, pp. 1445-83.

- Madhavan, Ananth. “Market Microstructure ▴ A Survey.” _Journal of Financial Markets_, vol. 3, no. 3, 2000, pp. 205-58.

- Lehalle, Charles-Albert, and Sophie Laruelle. _Market Microstructure in Practice_. World Scientific Publishing, 2013.

- Glosten, Lawrence R. and Paul R. Milgrom. “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders.” _Journal of Financial Economics_, vol. 14, no. 1, 1985, pp. 71-100.

![A precise digital asset derivatives trading mechanism, featuring transparent data conduits symbolizing RFQ protocol execution and multi-leg spread strategies. Intricate gears visualize market microstructure, ensuring high-fidelity execution and robust price discovery](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-rfq-execution-framework.jpg)

## Reflection

The integration of a request-for-quote protocol is an inflection point for a market-making firm. It compels a deep re-evaluation of the very definition of risk and the sources of competitive advantage. The architecture described here, a system of counterparty intelligence, dynamic pricing, and robust technology, is a blueprint for navigating this new landscape.

Yet, the blueprint itself is not the final structure. The true operational edge is found in the continuous process of refinement, adaptation, and learning that the system enables.

![Brushed metallic and colored modular components represent an institutional-grade Prime RFQ facilitating RFQ protocols for digital asset derivatives. The precise engineering signifies high-fidelity execution, atomic settlement, and capital efficiency within a sophisticated market microstructure for multi-leg spread trading](https://options.greeks.live/wp-content/uploads/2025/08/institutional-grade-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-system.jpg)

## How Will Your Framework Evolve?

Consider your own operational framework. Is it designed to learn from every interaction? Does it capture the subtle information contained in every quote request, every trade, and every missed opportunity? The transition to RFQ-based trading is a move towards a more intelligent, more relational form of market making.

The most successful firms will be those who build not just a superior pricing engine, but a superior learning engine. The future of liquidity provision will be defined by the ability to transform data into insight, and insight into a decisive, profitable execution. The system is the strategy.

![A glowing central ring, representing RFQ protocol for private quotation and aggregated inquiry, is integrated into a spherical execution engine. This system, embedded within a textured Prime RFQ conduit, signifies a secure data pipeline for institutional digital asset derivatives block trades, leveraging market microstructure for high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-system-for-institutional-high-fidelity-execution.jpg)

## Glossary

<a href="https://prime.greeks.live/area/central-limit-order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, split capsule object reveals an internal glowing teal light connecting its two halves, symbolizing a secure, high-fidelity RFQ protocol facilitating atomic settlement for institutional digital asset derivatives. This represents the precise execution of multi-leg spread strategies within a principal's operational framework, ensuring optimal liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-system-2.jpg)

## Central Limit Order Book

Meaning ▴ A Central Limit Order Book (CLOB) is a foundational trading system architecture where all buy and sell orders for a specific crypto asset or derivative, like institutional options, are collected and displayed in real-time, organized by price and time priority. </a><a href="https://prime.greeks.live/area/request-for-quote/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract spheres depict segmented liquidity pools within a unified Prime RFQ for digital asset derivatives. Intersecting blades symbolize precise RFQ protocol negotiation, price discovery, and high-fidelity execution of multi-leg spread strategies, reflecting market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-1.jpg)

## Request for Quote

Meaning ▴ A Request for Quote (RFQ), in the context of institutional crypto trading, is a formal process where a prospective buyer or seller of digital assets solicits price quotes from multiple liquidity providers or market makers simultaneously. </a><a href="https://prime.greeks.live/area/market-maker/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A transparent cylinder containing a white sphere floats between two curved structures, each featuring a glowing teal line. This depicts institutional-grade RFQ protocols driving high-fidelity execution of digital asset derivatives, facilitating private quotation and liquidity aggregation through a Prime RFQ for optimal block trade atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-6.jpg)

## Market Maker

Meaning ▴ A Market Maker, in the context of crypto financial markets, is an entity that continuously provides liquidity by simultaneously offering to buy (bid) and sell (ask) a particular cryptocurrency or derivative. </a><a href="https://prime.greeks.live/area/rfq-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A polished, light surface interfaces with a darker, contoured form on black. This signifies the RFQ protocol for institutional digital asset derivatives, embodying price discovery and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-interface.jpg)

## Rfq Protocol

Meaning ▴ An RFQ Protocol, or Request for Quote Protocol, defines a standardized set of rules and communication procedures governing the electronic exchange of price inquiries and subsequent responses between market participants in a trading environment. </a><a href="https://prime.greeks.live/area/pricing-strategy/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Visualizing institutional digital asset derivatives market microstructure. A central RFQ protocol engine facilitates high-fidelity execution across diverse liquidity pools, enabling precise price discovery for multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-across-multi-asset-liquidity.jpg)

## Pricing Strategy

Meaning ▴ Pricing strategy in crypto investing involves the systematic approach adopted by market participants, such as liquidity providers or institutional trading desks, to determine the bid and ask prices for crypto assets, options, or other derivatives. </a><a href="https://prime.greeks.live/area/adverse-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract geometric forms in blue and beige represent institutional liquidity pools and market segments. A metallic rod signifies RFQ protocol connectivity for atomic settlement of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-market-microstructure-1.jpg)

## Adverse Selection

Meaning ▴ Adverse selection in the context of crypto RFQ and institutional options trading describes a market inefficiency where one party to a transaction possesses superior, private information, leading to the uninformed party accepting a less favorable price or assuming disproportionate risk. </a><a href="https://prime.greeks.live/area/bid-ask-spread/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![The abstract image features angular, parallel metallic and colored planes, suggesting structured market microstructure for digital asset derivatives. A spherical element represents a block trade or RFQ protocol inquiry, reflecting dynamic implied volatility and price discovery within a dark pool](https://options.greeks.live/wp-content/uploads/2025/07/optimized-institutional-digital-asset-derivatives-rfq-protocol-framework-ensuring-principal-best-execution-and-latent-liquidity-capture.jpg)

## Bid-Ask Spread

Meaning ▴ The Bid-Ask Spread, within the cryptocurrency trading ecosystem, represents the differential between the highest price a buyer is willing to pay for an asset (the bid) and the lowest price a seller is willing to accept (the ask). </a><a href="https://prime.greeks.live/area/dynamic-spread/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Polished, curved surfaces in teal, black, and beige delineate the intricate market microstructure of institutional digital asset derivatives. These distinct layers symbolize segregated liquidity pools, facilitating optimal RFQ protocol execution and high-fidelity execution, minimizing slippage for large block trades and enhancing capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/precision-rfq-protocol-execution-institutional-digital-asset-derivatives-flow.jpg)

## Dynamic Spread

Meaning ▴ Dynamic Spread refers to the bid-ask spread that continuously adjusts in real-time based on prevailing market conditions, rather than remaining static. </a><a href="https://prime.greeks.live/area/order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Two distinct components, beige and green, are securely joined by a polished blue metallic element. This embodies a high-fidelity RFQ protocol for institutional digital asset derivatives, ensuring atomic settlement and optimal liquidity](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-protocol-for-digital-asset-derivatives-high-fidelity-link.jpg)

## Order Book

Meaning ▴ An Order Book is an electronic, real-time list displaying all outstanding buy and sell orders for a particular financial instrument, organized by price level, thereby providing a dynamic representation of current market depth and immediate liquidity. </a><a href="https://prime.greeks.live/question/how-does-sa-ccr-improve-upon-the-current-exposure-methodology/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central, blue-illuminated, crystalline structure symbolizes an institutional grade Crypto Derivatives OS facilitating RFQ protocol execution. Diagonal gradients represent aggregated liquidity and market microstructure converging for high-fidelity price discovery, optimizing multi-leg spread trading for digital asset options](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-11.jpg)

## Current Inventory

SA-CCR upgrades the prior method with a risk-sensitive system that rewards granular hedging and collateralization for capital efficiency. </a><a href="https://prime.greeks.live/area/block-trade/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Precision cross-section of an institutional digital asset derivatives system, revealing intricate market microstructure. Toroidal halves represent interconnected liquidity pools, centrally driven by an RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-architecture-revealing-precision-rfq-protocol-engine-for-optimal-high-fidelity-execution-and-latent-liquidity-aggregation.jpg)

## Block Trade

Meaning ▴ A Block Trade, within the context of crypto investing and institutional options trading, denotes a large-volume transaction of digital assets or their derivatives that is negotiated and executed privately, typically outside of a public order book. </a><a href="https://prime.greeks.live/area/inventory-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A dark blue sphere, representing a deep liquidity pool for digital asset derivatives, opens via a translucent teal RFQ protocol. This unveils a principal's operational framework, detailing algorithmic trading for high-fidelity execution and atomic settlement, optimizing market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/rfq-block-trade-liquidity-microstructure-execution-prime-os-derivatives-capital.jpg)

## Inventory Management

Meaning ▴ Inventory Management in crypto investing refers to the systematic and sophisticated process of meticulously overseeing and controlling an institution's comprehensive holdings of various digital assets, encompassing cryptocurrencies, stablecoins, and tokenized securities, across a distributed landscape of wallets, exchanges, and lending protocols. </a><a href="https://prime.greeks.live/area/inventory-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sophisticated metallic mechanism, split into distinct operational segments, represents the core of a Prime RFQ for institutional digital asset derivatives. Its central gears symbolize high-fidelity execution within RFQ protocols, facilitating price discovery and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/precision-rfq-engine-for-institutional-digital-asset-derivatives-trading.jpg)

## Inventory Risk

Meaning ▴ Inventory Risk, in the context of market making and active trading, defines the financial exposure a market participant incurs from holding an open position in an asset, where unforeseen adverse price movements could lead to losses before the position can be effectively offset or hedged. </a><a href="https://prime.greeks.live/question/could-excessive-dark-pool-trading-volume-destabilize-the-primary-public-exchanges/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, disc-shaped system, with concentric rings and a central dome, visually represents an advanced Principal's operational framework. It integrates RFQ protocols for institutional digital asset derivatives, facilitating liquidity aggregation, high-fidelity execution, and real-time risk management](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-engine-digital-asset-derivatives-execution.jpg)

## Public Market

Excessive dark pool volume can degrade public price discovery, creating a systemic feedback loop that undermines the stability of all markets. </a><a href="https://prime.greeks.live/area/trade-size/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A metallic ring, symbolizing a tokenized asset or cryptographic key, rests on a dark, reflective surface with water droplets. This visualizes a Principal's operational framework for High-Fidelity Execution of Institutional Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-rfq-protocol-execution.jpg)

## Trade Size

Meaning ▴ Trade Size, within the context of crypto investing and trading, quantifies the specific amount or notional value of a particular cryptocurrency asset involved in a single executed transaction or an aggregated order. </a><a href="https://prime.greeks.live/area/market-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sphere split into light and dark segments, revealing a luminous core. This encapsulates the precise Request for Quote RFQ protocol for institutional digital asset derivatives, highlighting high-fidelity execution, optimal price discovery, and advanced market microstructure within aggregated liquidity pools](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-execution-framework-for-institutional-digital-asset-derivatives-request-for-quote-protocol-optimization.jpg)

## Market Data

Meaning ▴ Market data in crypto investing refers to the real-time or historical information regarding prices, volumes, order book depth, and other relevant metrics across various digital asset trading venues. </a><a href="https://prime.greeks.live/question/how-does-dealer-selection-influence-the-severity-of-adverse-selection-in-illiquid-markets/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek Prime RFQ interface features a luminous teal display, signifying real-time RFQ Protocol data and dynamic Price Discovery within Market Microstructure. A detached sphere represents an optimized Block Trade, illustrating High-Fidelity Execution and Liquidity Aggregation for Institutional Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/08/advanced-rfq-protocol-engine-for-institutional-digital-asset-derivatives.jpg)

## Adverse Selection Spread

Strategic dealer selection is a control system that regulates information flow to mitigate adverse selection in illiquid markets. </a>

**Tags:** [Request for Quote](https://options.greeks.live/area/request-for-quote/), [Liquidity Sourcing](https://options.greeks.live/area/liquidity-sourcing/), [FIX Protocol](https://options.greeks.live/area/fix-protocol/), [Winner's Curse](https://options.greeks.live/area/winners-curse/), [Inventory Management](https://options.greeks.live/area/inventory-management/), [RFQ Protocol](https://options.greeks.live/area/rfq-protocol/)	

## Discover More

- 
### [What Are the Key Differences in Risk Management for RFQ versus CLOB Trading?](https://options.greeks.live/question/what-are-the-key-differences-in-risk-management-for-rfq-versus-clob-trading/)
![A crystalline sphere, representing aggregated price discovery and implied volatility, rests precisely on a secure execution rail. This symbolizes a Principal's high-fidelity execution within a sophisticated digital asset derivatives framework, connecting a prime brokerage gateway to a robust liquidity pipeline, ensuring atomic settlement and minimal slippage for institutional block trades.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-execution-platform-optimizing-liquidity-and-risk-management-through-advanced-protocols.jpg)

RFQ offers discreet, certain execution for large trades; CLOB provides anonymous, continuous trading for liquid markets.

- 
### [How Does the RFQ Workflow Mitigate the Risk of Information Leakage during Block Trades?](https://options.greeks.live/question/how-does-the-rfq-workflow-mitigate-the-risk-of-information-leakage-during-block-trades/)
![A segmented circular diagram, split diagonally. Its core, with blue rings, represents the Prime RFQ Intelligence Layer driving High-Fidelity Execution for Institutional Digital Asset Derivatives. Outer segments denote RFQ protocols and order flow pathways, reflecting Market Microstructure for Private Quotation.](https://options.greeks.live/wp-content/uploads/2025/08/high-speed-prime-os-rfq-digital-asset-derivatives-trade-execution.jpg)

The RFQ workflow mitigates information leakage by converting public order exposure into a controlled, private auction among trusted counterparties.

- 
### [What Are the Fundamental Differences in Price Discovery between an RFQ and a Lit Central Limit Order Book?](https://options.greeks.live/question/what-are-the-fundamental-differences-in-price-discovery-between-an-rfq-and-a-lit-central-limit-order-book/)
![Central translucent blue sphere represents RFQ price discovery for institutional digital asset derivatives. Concentric metallic rings symbolize liquidity pool aggregation and multi-leg spread execution. This market microstructure optimization within a Principal's Crypto Derivatives OS ensures high-fidelity execution and atomic settlement.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-high-fidelity-execution-1.jpg)

An RFQ provides discreet, negotiated liquidity for size, while a CLOB offers continuous, anonymous price discovery for all.

- 
### [How Does Adverse Selection Risk Differ between RFQ and CLOB Protocols?](https://options.greeks.live/question/how-does-adverse-selection-risk-differ-between-rfq-and-clob-protocols/)
![Polished metallic surface with a central intricate mechanism, representing a high-fidelity market microstructure engine. Two sleek probes symbolize bilateral RFQ protocols for precise price discovery and atomic settlement of institutional digital asset derivatives on a Prime RFQ, ensuring best execution for Bitcoin Options.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-engine-core-market-microstructure-intelligence-layer.jpg)

Adverse selection in a CLOB is a socialized, ambient risk priced into the spread; in an RFQ, it is a concentrated, bilateral risk priced by the dealer.

- 
### [Can the Use of RFQ Protocols in Illiquid Assets Create Systemic Risk during Volatile Periods?](https://options.greeks.live/question/can-the-use-of-rfq-protocols-in-illiquid-assets-create-systemic-risk-during-volatile-periods/)
![A central RFQ engine flanked by distinct liquidity pools represents a Principal's operational framework. This abstract system enables high-fidelity execution for digital asset derivatives, optimizing capital efficiency and price discovery within market microstructure for institutional trading.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-engine-optimal-execution-for-digital-asset-derivatives.jpg)

The use of RFQ protocols in illiquid assets can create systemic risk by concentrating hidden selling pressure on key dealers.

- 
### [Can the Use of RFQ Protocols Create New Forms of Adverse Selection Risk for Liquidity Providers?](https://options.greeks.live/question/can-the-use-of-rfq-protocols-create-new-forms-of-adverse-selection-risk-for-liquidity-providers/)
![Abstract geometric forms depict institutional digital asset derivatives trading. A dark, speckled surface represents fragmented liquidity and complex market microstructure, interacting with a clean, teal triangular Prime RFQ structure. This symbolizes high-fidelity execution via RFQ protocols, ensuring efficient price discovery and atomic settlement for block trades.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-system-for-prime-os-liquidity-pool.jpg)

RFQ protocols create new adverse selection risks by transforming the threat from a statistical market problem to a targeted counterparty risk.

- 
### [How Does an RFQ Handle Partial Fills?](https://options.greeks.live/question/how-does-an-rfq-handle-partial-fills/)
![Parallel execution layers, light green, interface with a dark teal curved component. This depicts a secure RFQ protocol interface for institutional digital asset derivatives, enabling price discovery and block trade execution within a Prime RFQ framework, reflecting dynamic market microstructure for high-fidelity execution.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-driven-institutional-digital-asset-derivatives-rfq-execution-framework.jpg)

An RFQ handles partial fills as a negotiated exception, not a systemic feature, requiring a new trade agreement.

- 
### [What Are the Key Differences between Rfq and Central Limit Order Book Transparency?](https://options.greeks.live/question/what-are-the-key-differences-between-rfq-and-central-limit-order-book-transparency/)
![Abstract layered forms visualize market microstructure, featuring overlapping circles as liquidity pools and order book dynamics. A prominent diagonal band signifies RFQ protocol pathways, enabling high-fidelity execution and price discovery for institutional digital asset derivatives, hinting at dark liquidity and capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-rfq-protocol.jpg)

RFQ offers discreet, negotiated liquidity for large or illiquid trades; CLOB provides continuous, transparent price discovery for standardized assets.

- 
### [How Can a Firm Quantify the Risk of Information Leakage in an RFQ Stress Test?](https://options.greeks.live/question/how-can-a-firm-quantify-the-risk-of-information-leakage-in-an-rfq-stress-test/)
![Abstract geometric forms depict multi-leg spread execution via advanced RFQ protocols. Intersecting blades symbolize aggregated liquidity from diverse market makers, enabling optimal price discovery and high-fidelity execution. The structured background suggests robust institutional-grade infrastructure and a sophisticated Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-multi-leg-rfq-execution-orchestration.jpg)

Quantifying RFQ leakage requires stress-testing dealer cohorts with controlled inquiries to measure adverse market impact and build a predictive risk model.

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    "description": "An RFQ protocol transforms a market maker's pricing from a public broadcast into a private, data-driven assessment of counterparty risk. \u25b4 Question",
    "url": "https://options.greeks.live/question/how-does-an-rfq-protocol-alter-the-pricing-strategy-of-a-market-maker/",
    "datePublished": "2025-07-31T12:42:11+00:00",
    "dateModified": "2025-10-23T14:43:42+00:00",
    "articleSection": [
        "Question"
    ],
    "author": {
        "@id": "https://greeks.live/#organization"
    },
    "publisher": {
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    "image": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-architecture-revealing-precision-rfq-protocol-engine-for-optimal-high-fidelity-execution-and-latent-liquidity-aggregation.jpg",
        "caption": "This abstract cross-section reveals the sophisticated market microstructure of an institutional digital asset derivatives. Dual toroidal segments symbolize aggregated liquidity pools and robust counterparty risk management. A central RFQ protocol engine orchestrates private quotation and best execution for block trades, enabling seamless multi-leg spread strategies and advanced portfolio margin calculations. This high-performance Prime RFQ architecture ensures low latency and optimal capital efficiency."
    },
    "keywords": [
        "Adverse Selection",
        "Algorithmic Trading",
        "Bid-Ask Spread",
        "Block Trade",
        "Central Limit Order Book",
        "Counterparty Risk",
        "Dynamic Spread",
        "FIX Protocol",
        "Inventory Management",
        "Inventory Risk",
        "Liquidity Sourcing",
        "Market Data",
        "Market Maker",
        "Market Microstructure",
        "Order Book",
        "Price Discovery",
        "Pricing Strategy",
        "Public Market Data",
        "Request for Quote",
        "RFQ Protocol",
        "Trade Size",
        "Winner's Curse"
    ]
}
```

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            "@type": "Question",
            "name": "What Is The Core Function Of Dynamic Spread Calibration?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "The spread quoted in an RFQ is not a static value. It is a dynamic calculation that must adapt in real time to a range of inputs. The static, one-size-fits-all spread of the CLOB is replaced by a multi-factor model. This model calibrates the bid-ask spread based on the specific context of each request."
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            "name": "How Does System Integration Impact Pricing Strategy?",
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                "text": "The execution of an RFQ pricing strategy is critically dependent on the seamless integration of various technology systems. The entire process, from receiving the RFQ to post-trade analysis, must be automated and operate at very low latencies. The technological architecture is the backbone that supports the entire operation."
            }
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                "text": "Consider your own operational framework. Is it designed to learn from every interaction? Does it capture the subtle information contained in every quote request, every trade, and every missed opportunity? The transition to RFQ-based trading is a move towards a more intelligent, more relational form of market making. The most successful firms will be those who build not just a superior pricing engine, but a superior learning engine. The future of liquidity provision will be defined by the ability to transform data into insight, and insight into a decisive, profitable execution. The system is the strategy."
            }
        }
    ]
}
```


---

**Original URL:** https://options.greeks.live/question/how-does-an-rfq-protocol-alter-the-pricing-strategy-of-a-market-maker/
