Risk-Sensitive Liquidity Management

Liquidity managers face inventory risks and financial shocks, requiring dynamic management and timely adjustments to funding rates to attract capital and maintain solvency buffers. Current methods struggle to adapt to market changes.

• An Integrated RWA Liquidity Engine: combines risk-sensitive optimization with multi-agent reinforcement learning to jointly manage AMM spreads and funding vault solvency.
• Risk-sensitive Markov decision process (MDP): captures terminal-stage reward risk and long-run process risk using measures such as variance, CVaR, VaR, and the Sharpe ratio, optimizing decision-making concerning the Sharpe ratio and solvency constraints.
• Validated through sandbox-style simulations: reflect realistic tokenized-deposit liquidity conditions, including inventory shocks, correlated credit events, and funding stress.

• The system will provide a governed, auditable, and production-ready solution for sustainable liquidity and funding management across tokenized private credit and broader RWA markets.

• The project offers auditable, production-ready solutions for sustainable liquidity and funding management in Tokenized private credit markets and broader RWA ecosystem.
• It advances policy optimization, convergence analysis and regret bounds, while extending from single-agent to multi-agent settings to model strategic interactions between liquidity providers and funding agents in blockchain environments.