Algorithmic Quote Skewing refers to the systematic adjustment of bid and ask prices offered by market makers, deviating from a neutral midpoint, based on specific risk factors or strategic objectives. This practice directly influences the two-sided quotes presented to counterparties in crypto Request for Quote (RFQ) systems or institutional options trading. The fundamental purpose is to manage exposure or capitalize on market conditions.
Mechanism
The operational logic involves dynamic pricing models that incorporate parameters such as inventory risk, directional market views, volatility expectations, and counterparty credit risk. These algorithms continuously re-calculate and publish quotes where the spread’s midpoint is shifted, presenting a less favorable price on one side of the market. This mechanism directly influences trade execution probabilities for various assets.
Methodology
The strategic approach behind quote skewing centers on optimizing risk-adjusted returns and capital utilization. Market makers employ this methodology to reduce over-concentration in specific assets, hedge against adverse price movements, or disincentivize trading activity that exacerbates existing positions. It represents a precise adjustment of market liquidity provision to align with a firm’s internal risk posture and trading strategy.
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