Institutional DeFi: Liquidity Pricing and Deal Design
This practitioner-led course teaches graduate students how institutional trading firms structure, price, and deliver liquidity solutions for digital asset protocols. The course bridges quantitative finance—options pricing, delta hedging, cost of capital analysis, KPI calibration—with the applied business of building and operating a liquidity provision practice.
Students will learn to evaluate deal economics using trading-desk metrics (ROC, cost of capital, capital velocity), value token-based compensation as embedded options and understand how market makers monetize those options through gamma scalping, design deliverable KPI schedules calibrated to empirical market data, analyze mechanism design risk across protocol architectures (redemption windows, bridge failures, oracle manipulation, validator concentration), and construct complete commercial proposals for multi-service protocol engagements. Case studies progress from structured computation to open-ended analysis and executive communication.
The central insight of the course is that institutional market making is increasingly a structured products business: trading firms deploy quantitative infrastructure not just to capture spread, but to fulfill contracted service obligations where the revenue is deterministic. A protocol paying $100K for a defined liquidity service is often more capital-efficient than deploying the same balance sheet into a trading strategy with a comparable expected return but meaningful variance. The quantitative challenge is in pricing these commitments correctly—knowing your cost of capital, modeling the embedded optionality in token compensation, understanding how to monetize that optionality through hedging, and designing KPIs that are simultaneously attractive to counterparties and deliverable by your trading desk. Critically, this course teaches students how to think using a quantitative and trader mindset, not to arrive at a single “correct” answer. In practice, deal structuring involves ambiguity, competing incentives, and imperfect information. The goal is to build a reasoning framework that produces defensible, well-justified proposals—not formulaic outputs.
Online Quarter: Summer 2026
Online Instructor: TBA