Systemic Importance of China's Financial Institutions: A Jump Volatility Spillover Network Review.

Xin Yang,Xian Zhao,Chuangxia Huang,Xu Gong,Xiaoguang Yang

doi:10.3390/e22050588

Abstract

The investigation of the systemic importance of financial institutions (SIFIs) has become a hot topic in the field of financial risk management. By making full use of 5-min high-frequency data, and with the help of the method of entropy weight technique for order preference by similarities to ideal solution (TOPSIS), this paper builds jump volatility spillover network of China’s financial institutions to measure the SIFIs. We find that: (i) state-owned depositories and large insurers display SIFIs according to the score of entropy weight TOPSIS; (ii) total connectedness of financial institution networks reveal that Industrial Bank, Ping An Bank and Pacific Securities play an important role when financial market is under pressure, especially during the subprime crisis, the European sovereign debt crisis and China’s stock market disaster; (iii) an interesting finding shows that some small financial institutions are also SIFIs during the financial crisis and cannot be ignored.

Highlights

With the development of economic globalization, the financial system has become more and more closely interconnected by investment networks, debtor–creditor and trade contacts [1,2,3,4]
We introduce the method of network construction and the indicator for assessing the systemic importance of financial institutions (SIFIs)
We establish jump volatility spillover network of financial institution according to the following three steps, where each financial institution represents a network node, and each pair of the financial institution is connected with an edge calculated by Granger-causality test

Summary

Introduction

With the development of economic globalization, the financial system has become more and more closely interconnected by investment networks, debtor–creditor and trade contacts [1,2,3,4]. Financial institutions such as depositories, broker-dealers and insurance companies permeate each other by related business and display significant complex network properties [5,6,7]. There are three ways to measure the SIFIs. The first way is to employ Pearson correlation coefficient to calculate the financial institutions’ default probabilities [17,18,19]. Adopting a tail-dependence method to measure the systemic risk contributions between financial institutions is the second method

Objectives

Methods

Findings

Conclusion