Cousaert, Simon;
Xu, Jiahua;
Matsui, Toshiko;
(2022)
SoK: Yield Aggregators in DeFi.
In:
2022 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).
IEEE: Shanghai, China.
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Abstract
Yield farming has been an immensely popular activity for cryptocurrency holders since the explosion of Decentralized Finance (DeFi) in the summer of 2020. In this Systematization of Knowledge (SoK), we study a general framework for yield farming strategies with empirical analysis. First, we summarize the fundamentals of yield farming by focusing on the protocols and tokens used by aggregators. We then examine the sources of yield and translate those into three example yield farming strategies, followed by the simulations of yield farming performance, based on these strategies. We further compare four major yield aggregators - Idle, Pickle, Harvest and Yearn - in the ecosystem, along with brief introductions of others. We systematize their strategies and revenue models, and conduct an empirical analysis with on-chain data from example vaults, to find a plausible connection between data anomalies and historical events. Finally, we discuss the benefits and risks of yield aggregators.
Type: | Proceedings paper |
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Title: | SoK: Yield Aggregators in DeFi |
Event: | 2022 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) |
Dates: | 2 May 2022 - 5 May 2022 |
ISBN-13: | 9781665495387 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/icbc54727.2022.9805523 |
Publisher version: | https://doi.org/10.1109/ICBC54727.2022.9805523 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10156119 |




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