AI 中文总结
本文通过序贯竞争限价订单簿模型，研究流动性需求尾部厚度如何影响大额交易的信息含量、价格冲击形状及价格发现速度。
AI 中文摘要
何时大额交易是新闻，何时是流动性冲击？我们在一个具有不对称信息的序贯竞争限价订单簿中研究这一问题。在我们的模型中，流动性提供者观察到总订单流，但无法分解为知情需求和不知情流动性需求。我们使用学生-t分布尾部对不知情订单流建模，解释为罕见流动性状态的简化形式。流动性需求的尾部指数决定了大额交易的信息含量。对于薄尾噪声，大额订单不平衡很快被解释为私人信息。对于厚尾流动性需求，相同的不平衡仍可能合理地被归因于流动性驱动。这种流动性尾部模糊性使价格冲击变得平坦且凹形，减缓了从订单流中学习的过程，并延迟了逆向选择溢价的下降。我们通过边际成本计划的不动点方程刻画均衡。厚尾流动性需求改变了均衡的数学性质：高斯单调性和紧致性论证失效，因为远程流动性状态在多项式阶上仍然与定价相关。我们在尾部控制的紧致类上构造不动点，并沿选定的单调分支研究学习和大型订单渐近性。在稳定的信息率条件下，重复订单流揭示了基本价值，但更厚的流动性尾部减缓了有限期限的价格发现。大订单冲击遵循正则变化渐近性，其指数取决于流动性尾部指数、知情竞争和后验信念。该模型将流动性尾部风险识别为市场冲击、价差弹性和大额交易信息含量的状态变量。
英文摘要
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.
