Abstract: We analyze the run risk of USD-backed stablecoins. Stablecoin issuers hold a portfolio of US dollar assets, while promising to redeem stablecoins for $1 with arbitrageurs on the primary market. Although arbitrage helps to stabilize the secondary market price at $1, we find that the largest stablecoin issuer, USDT, only trades with 6 authorized arbitrageurs in a given month. We show issuers actively constrain arbitrage because more efficient arbitrage amplifies the risk of panic runs by reducing investors' price impact from selling stablecoins in secondary markets. Our estimated model predicts a sizable run risk for the largest two USD stablecoins, Tether (USDT) and Circle (USDC). These stablecoin runs could disrupt important USD markets for bank deposits and Treasuries. Finally, we show that run risk could be reduced by imposing redemption fees or issuing dividends to investors.
Jiageng Liu, Massachusetts Institute of Technology
Igor Makarov, London School of Economics and Political Science
Antoinette Schoar, Massachusetts Institute of Technology
Abstract: This paper provides a comprehensive analysis of the Terra-Luna network leadingup to and during the run on its stablecoin (UST) in May 2022. The Terra caseunderscores the limitations inherent in private money creation. We documentthat the crash was preceded by the rapidly deteriorating economic fundamentalsof the network, but the system's complexity made it difficult even for insidersto assess the buildup of risk and adjust system parameters accordingly. Decentralizedgovernance mechanisms added inefficiencies to the system and furtherexacerbated the instability. Once a few large wallets started withdrawing theirfunds, wealthier and more sophisticated investors processed information moreefficiently, ran more decisively, and realized much smaller losses. Our resultssuggest that blockchain transparency may not only fail to create a level playingfield but can exacerbate asymmetries between investors.
Christine Parlour, University of California-Berkeley
Johan Walden, University of California-Berkeley
Abstract: We posit a fundamental value pricing equation for an asset with unobserved, time-varying yield and unobserved, time-varying crash risk. We provide a technique to identify sets of parameters consistent with both fundamental value pricing and the observed price series. This technique performs as a semi-parametric test for bubbles. Our test performs well for benchmark examples that bubble tests based on unit roots or explosive behavior find hard to identify. We apply our method to traditional stocks and stock indices. When applied to cryptocurrencies, the test suggests the presence of a bubble for several major cryptocurrencies at risk adjusted discount rates up to 25% per year, and even at 50% per year. In particular, for Ethereum very high risk adjusted discount rates, above 64% per year, are needed for our test to fail to reject the no-bubble null hypothesis.
Discussant: Gustavo Schwenkler, Santa Clara University
Mehmet Ihsan Canayaz, Pennsylvania State University
Charles Cao, Pennsylvania State University
Giang Nguyen, Pennsylvania State University
Qiang Wang, Pennsylvania State University
Abstract: We investigate the formation of cryptocurrency sentiment and its subsequent influenceon cryptocurrency returns. Utilizing an extensive dataset encompassing emotions, priceperceptions, and fundamental events for 267 cryptocurrencies, accounting for 95% ofthe total cryptocurrency market capitalization, we find that social media sentimentpredicts cryptocurrency returns, but news media sentiment does not. Fundamentalevents shape sentiment, but it is the part of sentiment unrelated to fundamental eventsthat significantly predicts returns. Overall, our study highlights social media’s crucialrole in information dissemination and sentiment formation in a market dominated byretail, social-media-active investors.
