Disentangling global equity market instability: a network analysis

TL;DR

This paper introduces a network analysis framework using daily international market data to identify sources of systemic instability and develop a financial stability indicator during crises.

Contribution

It presents a novel network-based approach incorporating conditional probabilities, flow measurements, and complexity analysis to assess systemic risk in global equity markets.

Findings

Network complexity and node contributions often offset each other in stability changes.

Changes in network edges are key determinants of systemic stability.

Total flow in the network correlates strongly with market volatility.

Abstract

During a financial crisis, the capital markets network frequently exhibits a high correlation between returns. We developed a network analysis framework based on daily returns from 42 countries to determine systemic stability. Our network is built using the conditional probability of co-movement of returns, and it identifies nodes, network complexity, and edge as potential sources of fragility. We also introduce the concept of measuring flows from one return to another. Then, we use 120-day rolling data to capture the financial system's behavior and create a financial stability indicator. We discover that the contributions of nodes and network complexity to changes in system stability frequently cancel each other out. Edge change may be a determinant of systemic stability. Furthermore, the total flows in the network are highly correlated with the volatility. It main advantage is the tractability and potential sources of volatility can be determined.