The crash of various stablecoins led to continuous adjustments of their design, most recently by backing algorithmic stablecoins with cryptocurrency pools. However, as this seems to be more of a trial-and- error process, the aim of this work is to support design decisions on their peg stability mechanism by an agent-based simulation model as a base to forecast the probability of a stablecoin run dependent on market participants’ confidence levels. Our model is tailored to the algorithmic stablecoin USDD which is i.a. pegged to the Fiat-backed stablecoin USDT and hence to the USD. As main result of our numerical study, stability depends on the price and volatility assessment of market participants and a stablecoin run can’t be prevented for sure. Methodologically, this work belongs to design science research, even though empirical market data is used to calibrate the simulation model, which can be used as base for design recommendations.
Baumeister, Alexander and Hägele, Sascha, "CONFIDENCE MATTERS: A SIMULATION-BASED STABILITY ANALYSIS OF ALGORITHMIC STABLECOINS" (2023). ECIS 2023 Research-in-Progress Papers. 6.