The RFQ Process, or Request for Quote process, is a formalized method of obtaining bespoke price quotes for a specific financial instrument, wherein a potential buyer or seller solicits bids from multiple liquidity providers before committing to a trade. It is a prevalent mechanism in OTC markets.
Mechanism
Operationally, a client initiates an RFQ by specifying the crypto asset, desired quantity, and transaction side. This request is electronically transmitted to a selected group of market makers or principal trading firms, who then respond with firm, executable two-sided prices (bid and ask) within a predefined time window. The client subsequently chooses the most competitive quote for execution.
Methodology
The methodology of the RFQ process is strategically advantageous for institutional crypto traders, particularly for large block trades or illiquid assets, as it facilitates competitive pricing, minimizes market impact, and provides access to deep, often off-exchange, liquidity. It is fundamental in institutional crypto options trading and OTC markets, enabling participants to achieve best execution by comparing multiple bids and offers.
Dealer selection in an RFQ is the architectural design of a private liquidity network; its quality dictates information control and execution certainty.
Markout analysis provides a quantitative diagnostic for identifying information leakage by measuring post-trade price drift, enabling systemic counterparty evaluation.
A fully on-chain RFQ process introduces novel security risks at the smart contract, oracle, and network layers that require a multi-faceted and proactive security strategy to mitigate.
Information leakage in a sequential RFQ forces a dealer's quoting strategy to evolve from simple pricing to a dynamic risk calculation based on their inferred position in the information cascade.
The trader's role evolves from transactional execution to strategic oversight, managing complex trades and client relationships while leveraging AI for automation.
Machine learning mitigates RFQ information leakage by predictively modeling dealer behavior to optimize counterparty selection and minimize market impact.
Dealer capital is the balance sheet commitment that underwrites a firm's capacity to absorb risk and provide immediacy in the bilateral fixed-income RFQ process.
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