市场的条件表示：均衡、拥挤与惯例多样性
The Market's Conditioning Representation: Equilibrium, Crowding, and Convention Multiplicity
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中文总结 AI 辅助
本文将市场条件架构内生化，提出表示均衡框架区分头寸与表示拥挤，证明均衡存在性等，发现谱统计量确定头寸路径机制，中介对冲可产生无限响应，最小二乘学习使主导惯例中性。
中文摘要 AI 辅助
资产定价模型通常以固定的信息集为条件。本文通过允许投资组合选择其诱导风险敞口会影响价格的表示，将市场的条件架构内生化。配置在各表示上的资本决定了总头寸与清算溢价，而价格反馈会改变表示的价值，并通过因果认证确定可接受的表示。表示均衡是该配置——价格——认证循环的不动点。该框架将头寸拥挤（通过市场冲击产生）与表示拥挤（由驱动空间重叠产生，通过基不变信息容量成本定价）区分开来。在紧性、预解正则性和连续认证条件下，均衡在任意转换成本下均存在；在稳定认证单元内，信息拥挤会产生凹种群博弈，且在小增益条件下局部唯一。在既定配置下，结合交叉冲击、协方差与部署容量的谱统计量可确定三种头寸路径机制：低于基本解的一半时，解唯一；在边界处，仅保留无创新偏差；高于该值时，不稳定方向支持连续的自证实惯例。单调冲击位于唯一性区域内，仅无限冲击不足以产生多样性。中介对冲可产生无限响应，而往返仍成本高昂。内生风险容量限制惯例幅度，最小二乘学习使主导惯例呈中性而非吸引性。阈值未在数据中估计；相反，实证研究记录了与表示拥挤一致的、特定驱动的样本外特征，并说明了直接测量所需的条件。
英文摘要
Asset-pricing models typically condition on a fixed information set. This paper endogenises the market's conditioning architecture by allowing portfolios to choose representations whose induced exposures affect prices. Capital allocated across representations determines aggregate positions and the clearing premium, while price feedback changes representation value and, through causal certification, admissible representations. A representation equilibrium is the fixed point of this configuration--price--certification loop. The framework separates position crowding through market impact from representation crowding generated by driver-space overlap and priced through a basis-invariant information-capacity cost. Under compactness, resolvent regularity and continuous certification, equilibrium exists at every switching cost; within a stable certification cell, informational congestion yields a concave population game and local uniqueness under a small-gain condition. At a settled configuration, a spectral statistic combining cross-impact, covariance and deployed capacity determines three position-path regimes: below one half the fundamental solution is unique; at the boundary only innovation-free deviations remain; above it, destabilising directions support a continuum of self-confirming conventions. Monotone impact lies inside the uniqueness region, while indefinite impact alone is not sufficient for multiplicity. Intermediary hedging can generate an indefinite response while round trips remain costly. Endogenous risk capacity bounds convention amplitudes, and least-squares learning makes the dominant convention neutral rather than attracting. The threshold is not estimated in data; instead, the empirical exercise documents an out-of-sample, driver-specific signature consistent with representation crowding and states the conditions required for direct measurement.
