Minsky Financial Instability, Interscale Feedback, Percolation and Marshall-Walras Disequilibrium

TL;DR

This paper models Minsky financial instability through a complex interplay of feedback loops, network contagion, and phase transitions, providing insights into the conditions leading to economic crises.

Contribution

It introduces an analytical and numerical framework combining network percolation and feedback loops to understand Minsky instability and its dynamic regimes.

Findings

Multiple fixed points lead to different dynamic regimes.

Network effects can cause phase transitions in financial stability.

Instability arises from the instability of fixed points in the feedback system.

Abstract

We study analytically and numerically Minsky instability as a combination of top-down, bottom-up and peer-to-peer positive feedback loops. The peer-to-peer interactions are represented by the links of a network formed by the connections between firms, contagion leading to avalanches and percolation phase transitions propagating across these links. The global parameter in the top-bottom, bottom-up feedback loop is the interest rate. Before the Minsky moment, in the Minsky Loans Accelerator stage, the relevant bottom parameter representing the individual firms micro-states is the quantity of loans. After the Minsky moment, in the Minsky Crisis Accelerator stage, the relevant bottom parameters are the number of ponzi units / quantity of failures, defaults. We represent the top-bottom, bottom-up interactions on a plot similar to the Marshal-Walras diagram for quantity-price market equilibrium (where the interest rate is the analog of the price). The Minsky instability is then simply emerging as a consequence of the fixed point (the intersection of the supply and demand curves) being unstable (repulsive). In the presence of network effects, one obtains more than one fixed point and a few dynamic regimes (phases). We describe them and their implications for understanding, predicting and steering economic instability.