Network Entropies of the Chinese Financial Market

Shouwei Li,Jianmin He,Kai Song

doi:10.3390/e18090331

Abstract

Based on the data from the Chinese financial market, this paper focuses on analyzing three types of network entropies of the financial market, namely, Shannon, Renyi and Tsallis entropies. The findings suggest that Shannon entropy can reflect the volatility of the financial market, that Renyi and Tsallis entropies also have this function when their parameter has a positive value, and that Renyi and Tsallis entropies can reflect the extreme case of the financial market when their parameter has a negative value.

Highlights

Before the recent financial crisis, the economic/financial network was mentioned only infrequently.it has caught the attention of both academics and policy makers [1,2]
Mantegna [4] first represents the financial market as a network, and observes the hierarchical structure in the US stock market based on the correlation matrix of asset returns, where a minimum spanning tree is generated in order to select the most important connections
If the window size is not very large compared to the number of stocks, one should expect that the determination of the covariance is noisy, and that the correlation matrix is to a large extent random, i.e., the structure of the matrix is dominated by measurement noise [36]

Summary

Introduction

Before the recent financial crisis, the economic/financial network was mentioned only infrequently. It has caught the attention of both academics and policy makers [1,2]. The financial market is an example of a complex system, and can be represented as a network This allows for the analysis of the financial market based on methods and tools of the complex network theory. Mantegna [4] first represents the financial market as a network, and observes the hierarchical structure in the US stock market based on the correlation matrix of asset returns, where a minimum spanning tree is generated in order to select the most important connections

Methods

Results

Conclusion