The local Gaussian correlation networks among return tails in the Chinese stock market

TL;DR

This paper introduces a novel financial network analysis method using local Gaussian correlation coefficients to better understand tail dependencies in the Chinese stock market, revealing heightened risk sensitivity in negative tail networks.

Contribution

The study applies local Gaussian correlation to construct tail-specific financial networks, providing more accurate insights into market risks compared to traditional Pearson correlation methods.

Findings

Negative tail networks are more sensitive to market risks.

Local Gaussian correlation captures nonlinear dependence and heavy tails.

Recommends prioritizing negative tail analysis for risk assessment.

Abstract

Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages including capturing local nonlinear dependence and handling heavy-tailed distributions. This study constructs financial networks using the local Gaussian correlation coefficients between tail regions of stock returns in the Shanghai Stock Exchange. The work systematically analyzes fundamental network metrics including node centrality, average shortest path length, and entropy. Compared with the local Gaussian correlation network among positive tails and the conventional Pearson correlation network, the properties of the local Gaussian correlation network among negative tails are more sensitive to the stock market risks. This finding suggests researchers should prioritize the local Gaussian correlation network among negative tails. Future work should reevaluate existing findings using the local Gaussian correlation method.