The Privacy Subsidy: Kyle's λ under Noise-Perturbed Order-Flow Observation

Battle Count: 78

Highly relevant to market-making strategies and AMM design in cryptocurrency markets. The privacy subsidy formula directly informs fee structures for privacy-preserving exchanges and LP compensation. Relevant to adverse selection modeling in DeFi, but primarily a theoretical/welfare result rather than a trading signal or execution algorithm. More applicable to exchange protocol design than to active trading strategy development.

The core results are closed-form algebraic expressions: λ = σv/(2√(σ²u + σ²ε)), β = √(σ²u + σ²ε)/σv, |πM| = σvσ²ε/(2√(σ²u + σ²ε)). Implementation requires only basic arithmetic and square roots. The challenge lies in calibrating σv, σu, and σε from real market data and protocol parameters, not in computing the formulas themselves.

Fully theoretical paper with closed-form analytical results. All proofs are self-contained and verifiable. Key formulas (λ, β, |πM|) are explicit functions of (σv, σu, σε). Numerical tables provided for calibration. No code or data required for verification.