Level: MA 
Responsible Person: Tao Yan
Keywords: Ethereum, DeFi, network science, systematic risk, simulation 

The DeFi ecosystem has grown significantly, However, due to the close interrelation among various DeFi platforms, there is a high risk of leverage leading to substantial losses.  Unlike the traditional financial system, where "too big to fail" applies, there is no regulation in the DeFi world, it remains unknown what the overall risk level of the DeFi system is and the outcome if major DeFi platforms were to suddenly crash. 

This project aims at evaluating the current DeFi system's level of risk and simulating the risk contagion. Initially, a large-scale dataset containing transactions between DeFi platforms will be collected from Ethereum. Then, a DeFi platform network will be created using this transaction data. Each DeFi platform will be assigned a risk factor after an evaluation. Finally, a simulation model will be developed using the network and risk factors to estimate the spillover of risk, this can be performed using epidemic models or traditional financial models. 

The ideal applicant for this project should possess knowledge or enthusiasm for collecting blockchain data, have a background in network science and finance, and be proficient in Python programming. 

References: 

• Tovanich, N., Kassoul, M., Weidenholzer, S., & Prat, J. (2023, November). Contagion in Decentralized Lending Protocols: A Case Study of Compound. In Proceedings of the 2023 Workshop on Decentralized Finance and Security (pp. 55-63). 
• Bekemeier, F. (2021, December). Deceptive assurance? a conceptual view on systemic risk in decentralized finance (DeFi). In Proceedings of the 2021 4th International Conference on Blockchain Technology and Applications (pp. 76-87).