Adaptive financial networks with static and dynamic thresholds

TL;DR

This paper investigates how adaptive thresholds in financial networks stabilize topology and reveal long-range correlations using stock market data from the US and China.

Contribution

It introduces a dynamic threshold approach that reduces fluctuations and uncovers stable network properties in financial markets.

Findings

Dynamic thresholds suppress large fluctuations in network topology.

Long-range correlations are observed in network metrics.

Degree distribution exhibits a two-peak behavior.

Abstract

Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads to a stable topological structure in the dynamic evolution. Long-range time-correlations are revealed for the average clustering coefficient, average degree and cross-correlation of degrees. The dynamic network shows a two-peak behavior in the degree distribution.