发表机构
The Hong Kong University of Science and Technology; Shandong University, Zhongtai Securities Institute for Financial Studies（香港科技大学; 山东大学，中泰证券金融研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究动态均值-方差偏好下知情交易者的时间不一致问题，通过前向-后向ODE系统和打靶法求解均衡，发现价格冲击呈下降期限结构，信息揭示和利润提前。
AI 中文摘要
我们研究了一个Kyle-Back模型，其中知情交易者具有动态均值-方差偏好，导致一个时间不一致的交易问题，该问题被表述为一种人内博弈。该模型结合了两个均衡要求：知情交易者与做市商之间的交易-定价均衡，以及交易者连续自我之间的时间一致均衡。遵循Cho（2003）的框架，我们考虑了风险中性和风险厌恶的知情交易者。在风险中性情况下，我们明确推导了均衡交易策略和价格冲击。在风险厌恶情况下，我们建立了存在性，并通过非线性常微分方程耦合系统刻画了均衡。我们的主要技术是将均衡条件简化为前向-后向常微分方程组，并通过打靶法解决由此产生的边界条件。从经济角度看，均值-方差偏好重塑了利用当前私人信息与保留未来信息优势之间的跨期权衡，产生了价格冲击的下降期限结构，并将信息揭示和知情交易利润转移到交易周期的早期阶段。
英文摘要
We study a Kyle--Back model in which the informed trader has dynamic mean--variance preferences, leading to a time-inconsistent trading problem that is formulated as an intrapersonal game. The model combines two equilibrium requirements: a trading--pricing equilibrium between the informed trader and the market maker, and a time-consistent equilibrium among the trader's successive selves. Following the framework of Cho (2003), we consider both risk-neutral and risk-averse informed traders. In the risk-neutral case, we derive the equilibrium trading strategy and price impact explicitly. In the risk-averse case, we establish existence and characterize the equilibrium through a coupled system of nonlinear ordinary differential equations. Our main technique is to reduce the equilibrium conditions to a forward--backward ODE system and resolve the resulting boundary conditions by a shooting argument. Economically, the mean--variance preference reshapes the intertemporal trade-off between exploiting current private information and preserving future informational advantage, generating a declining term structure of price impact and shifting both information revelation and informed-trading profits toward earlier stages of the trading horizon.
