标题： 多尺度网络动态与系统性风险：金融市场的模型上下文协议方法 显示英文标题

标题： Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets

摘要： 本文介绍了一种新颖的框架，通过使用模型上下文协议（MCP）进行代理通信，利用多尺度网络动力学分析金融市场中的系统性风险。 我们开发了一种综合方法，结合转移熵网络、基于代理的建模和小波分解，以在MCPFM（模型上下文协议金融市场）R包中实现跨时间尺度的信息流捕捉。 我们的方法使包括高频交易者、做市商、机构投资者和监管者的异质金融代理能够通过结构化协议进行通信，同时保持现实的市场微观结构。 实证分析表明，我们的多尺度方法揭示了以前隐藏的系统性风险模式，所提出的系统性风险指数相比传统措施具有更优越的早期预警能力。 该框架为宏观审慎政策设计和监管干预策略提供了新的见解。 完整的实现作为开源R包在https://github.com/avishekb9/MCPFM提供，以促进可重复的研究和实际应用。 显示英文摘要

摘要： This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication. We develop an integrated approach that combines transfer entropy networks, agent-based modeling, and wavelet decomposition to capture information flows across temporal scales implemented in the MCPFM (Model Context Protocol Financial Markets) R package. Our methodology enables heterogeneous financial agents including high-frequency traders, market makers, institutional investors, and regulators to communicate through structured protocols while maintaining realistic market microstructure. The empirical analysis demonstrates that our multi-scale approach reveals previously hidden systemic risk patterns, with the proposed systemic risk index achieving superior early warning capabilities compared to traditional measures. The framework provides new insights for macroprudential policy design and regulatory intervention strategies. The complete implementation is available as an open-source R package at https://github.com/avishekb9/MCPFM to facilitate reproducible research and practical applications.