TY - GEN
T1 - Thinking Fast and Slow
T2 - 6th Conference on Advances in Financial Technologies, AFT 2024
AU - Bastankhah, Mahsa
AU - Nadkarni, Viraj
AU - Jin, Chi
AU - Kulkarni, Sanjeev Ramesh
AU - Viswanath, Pramod
N1 - Publisher Copyright: © Mahsa Bastankhah, Viraj Nadkarni, Chi Jin, Sanjeev Kulkarni, and Pramod Viswanath.
PY - 2024/9
Y1 - 2024/9
N2 - Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn’t adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits.
AB - Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn’t adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits.
KW - adaptive market design
KW - decentralized finance
KW - Defi borrow-lending
UR - http://www.scopus.com/inward/record.url?scp=85204423352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204423352&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.AFT.2024.27
DO - 10.4230/LIPIcs.AFT.2024.27
M3 - Conference contribution
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 6th Conference on Advances in Financial Technologies, AFT 2024
A2 - Bohme, Rainer
A2 - Kiffer, Lucianna
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Y2 - 23 September 2024 through 25 September 2024
ER -