Information Structures in Stablecoin Markets

Stablecoins have historically depegged due from par to large sales, possibly of speculative nature, or poor reserve asset quality. Using a global game which addresses both concerns, we show that the selling pressure on stablecoin holders increases in the presence of a large sale. While precise public knowledge reduces (increases) the probability of a run when fundamentals are strong (weak), interestingly, more precise private signals increase (reduce) the probability of a run when fundamentals are strong (weak), potentially explaining the stability of opaque stablecoins. The total run probability can be decomposed into components representing risks from large sales and poor collateral. By analyzing how these risk components vary with respect to information uncertainty and fundamentals, we can split the fundamental space into regions based on the type of risk a stablecoin issuer is more prone to. We suggest testable implications and connect our model’s implications to real-world applications, including depegging events and the no-questions-asked property of money.

💡 Research Summary

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The paper “Information Structures in Stablecoin Markets” develops a global‑game framework to study how two distinct sources of risk—large‑scale redemptions (or sales) and poor collateral quality—affect the probability that a stablecoin loses its peg. The authors extend the canonical stablecoin run model by (i) inserting an exogenous “large seller” who, with probability π, liquidates a mass δ of the token, and (ii) allowing agents to receive both a public signal and a private noisy signal about the underlying reserve quality θ.

In the complete‑information version, the issuer’s solvency condition is λ < θ, where λ is the total mass of agents who redeem. If the large seller does not act, the unique equilibrium is “all hold” when θ > 1 − δ; otherwise multiple equilibria (all hold or all sell) can arise, reproducing the classic self‑fulfilling run dynamics. When a large sale occurs, the region where “all hold” is an equilibrium shrinks, and a new “all‑sell” region appears, showing that the mere possibility of a big redemption raises selling pressure for any given θ.

To resolve multiplicity, the authors turn to a global game. Nature draws θ ∼ N(μ,σ²). Each small investor i observes a private signal x_i = θ + σ_ε ε_i with ε_i ∼ N(0,σ_x²). Public information is represented by the precision of the aggregate signal (σ_public), while private information precision is σ_private. Investors adopt a threshold strategy: they redeem if and only if their signal falls below a cutoff (\bar{x}). The equilibrium cutoff is determined by the fixed‑point condition that the expected proportion of redeemers λ(θ; (\bar{x})) exactly matches the solvency threshold. The authors prove existence and uniqueness of such an equilibrium for both the “large‑sale” and “no‑large‑sale” states.

The comparative‑static analysis yields several striking results:

1. Public‑signal precision: When the public signal becomes more precise (σ_public ↓), the run probability falls if fundamentals are strong (θ sufficiently above δ) because agents can coordinate on holding. Conversely, when fundamentals are weak, higher public precision raises the run probability by making the weakness more common knowledge.

2. Private‑signal precision: More precise private signals (σ_private ↓) increase heterogeneity in beliefs. This heterogeneity raises the run probability when fundamentals are strong (agents with pessimistic private draws trigger early redemptions) but lowers it when fundamentals are weak (optimistic private draws keep some agents from selling). This counter‑intuitive effect suggests that opacity—i.e., less precise public disclosure combined with diverse private assessments—can actually stabilize a token.

3. Risk decomposition: The total run probability is split into two components: (i) the “large‑sale risk” – the conditional probability of a run given that the large seller has acted, and (ii) the “collateral‑quality risk” – the conditional probability when the large seller is absent. The first component is driven mainly by the size of δ and the probability π, while the second depends on the distribution of θ and the information structure.

4. Fundamental‑information space partition: By varying θ, σ_public, and σ_private, the authors map four regions: (a) large‑sale risk dominates (θ ≤ δ), (b) collateral‑quality risk dominates (θ ≫ δ but public precision low), (c) both risks comparable (intermediate θ and precisions), and (d) negligible risk (θ ≫ δ, both signals precise). This partition provides a diagnostic tool for issuers and regulators to identify which risk channel is most threatening for a given stablecoin.

The paper connects the theory to real‑world events. The 2023 Silicon Valley Bank collapse caused a temporary de‑pegging of USDC, a highly transparent stablecoin, illustrating that even perfect public disclosure cannot shield against an exogenous large‑sale shock. Tether (USDT), with sparse public audits but a broad base of private assessments, has survived multiple speculative attacks, consistent with the model’s “opaque but heterogeneous” stability channel. TerraUSD (UST), whose algorithmic design effectively created a built‑in large‑sale pressure, collapsed when the market’s fundamentals weakened, matching the “large‑sale‑dominant” region.

Policy implications follow naturally. Transparency mandates (e.g., the U.S. GENIUS Act, EU MiCA) are most effective when a token’s collateral is already strong; otherwise, they may amplify panic. Regulators might consider a tiered disclosure regime that ties reporting frequency and depth to measurable collateral ratios. Moreover, encouraging a diversified set of auditors or allowing limited opacity could foster the beneficial heterogeneity highlighted by the private‑signal result. Finally, issuers should maintain liquidity buffers specifically sized to absorb a potential δ‑mass redemption, as the model shows that the mere possibility of such a shock materially shifts equilibrium behavior.

In sum, the article offers a rigorous, analytically tractable model that disentangles how public and private information, together with the threat of a large redemption, shape stablecoin stability. It delivers both theoretical insights—novel comparative statics on information precision—and practical guidance for policymakers and market participants navigating the evolving stablecoin ecosystem.