Heavy-Tailed Liquidity Demand, Price Discovery, and Limit Order Book Risk

Summary

This paper studies how heavy-tailed uninformed trading affects price discovery in a sequential limit order book where traders may have private information. Liquidity suppliers see aggregate order flow but cannot directly distinguish informed orders from uninformed liquidity shocks. When uninformed demand has heavy tails, a large trade can remain plausibly uninformed at greater depths than under Gaussian assumptions. This flattens price impact and slows learning, although sufficiently extreme trades can still signal private information.

The authors characterize equilibrium with a nonlinear fixed-point equation and establish existence under tail controls, alongside posterior consistency and asymptotic results for costs and order flow. They also derive eventual dominance by informed demand and monotonicity of the book in the far tails. An empirical analysis of ten-level AAPL data reports farther-out crossover diagnostics and persistent bid-ask spreads after large heavy-tailed trades. The model and evidence focus on a particular information and liquidity setting; the abstract does not establish that the same patterns hold across assets, venues, or other order book mechanisms.

Key ideas

• Heavy-tailed uninformed order flow can make large trades less immediately informative about private information.
• The resulting price impact is flatter and learning by liquidity suppliers is slower over a wider range of depths.
• Extreme trades can still become informative as informed demand dominates in the tails.
• The paper establishes equilibrium properties using a tail-controlled fixed-point approach.
• Ten-level AAPL data show crossover diagnostics and persistent spreads after large heavy-tailed trades.

Tags

# When large trades are not (automatically) news: liquidity tail risk and price discovery


# When large trades are not (automatically) news: liquidity tail risk and price discovery









We examine how heavy-tailed liquidity demand changes price discovery in a sequential limit order book with asymmetric information. In our setting, liquidity suppliers observe aggregate order flow, not its decomposition into informed demand and uninformed liquidity shocks. With heavy-tailed uninformed aggregated order flow, large trades remain plausibly uninformed over a wider range of depths, flattening price impact and slowing learning; sufficiently extreme trades can nevertheless become informative. We characterize equilibrium through a non-linear fixed point equation for the marginal-cost schedule; heavy-tailed uninformed aggregated order flow invalidates the monotonicity and compactness arguments available under Gaussianity. Therefore, we establish fixed-point existence within a tail-controlled class, prove posterior consistency for liquidity suppliers in the presence of endogenous dependent order flow, and derive tail asymptotics for marginal costs, informed demand, and aggregate order flow. Additionally, we obtain eventual informed-demand dominance and eventual monotonicity of the book in the far tails. Empirically, using 10-level AAPL data, we document farther-out crossover diagnostics and persistent bid-ask spreads following large heavy-tailed trades.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.