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

TL;DR

This paper presents a multi-scale network dynamics framework using Model Context Protocol to analyze systemic risk in financial markets, improving early warning capabilities and offering new insights for regulation.

Contribution

It introduces a novel multi-scale approach combining transfer entropy, agent-based modeling, and wavelet analysis with MCP for systemic risk assessment.

Findings

Reveals hidden systemic risk patterns across scales

Achieves superior early warning performance

Provides an open-source R package for implementation

Abstract

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.