Narrow Results

One of the most characteristic features of the global financial network is its inherently complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic methods turned out to be powerful for the purpose of analytically describing the contagion process and making statements about resilience. So far, however, such methods have been limited to so-called rank-one models in which, informally speaking, the only parameter for the skeleton of the network is the degree sequence and the contagion process can be described by a one-dimensional fixed-point equation. Such networks fail to account for the possibility of a pronounced block structure such as core/periphery or a network composed of different connected blocks for different countries. We present a much more general model here, where we distinguish vertices (institutions) of different types and let edge probabilities and exposures depend on the types of both, the receiving and the sending vertex, plus additional parameters. Our main result allows one to compute explicitly the systemic damage caused by some initial local shock event, and we derive a complete characterization of resilient and nonresilient financial systems. This is the first instance that default contagion is rigorously studied in a model outside the class of rank-one models and several technical challenges arise. In contrast to previous work, in which networks could be classified as resilient or nonresilient independently of the distribution of the shock, information about the shock becomes important in our model and a more refined resilience condition arises. Among other applications of our theory we derive resilience conditions for the global network based on subnetwork conditions only.

We study volatility spillovers from the Chinese A-share market to four Asia-Pacific (APAC) markets and three global markets during the Chinese stock market crisis. We make use of a nonlinear model and determine that volatility spillovers tend to be regional, posing greater risks to the region than elsewhere. We show that, during the crisis, the Chinese stock market is more integrated in the APAC region. We find no evidence of asymmetric effects and exclude short-run effects of the national team established by the Chinese authorities. We construct a volatility spillover balance and find that, during the financial turbulence, mainland China changes its status from being volatility spillover receiver to volatility generator.

We analyze the volatility spillover effect from the Chinese stock market to different stock markets in the G20 countries. We employ dynamic conditional correlation and vector autoregression (VAR) to analyze adjusted daily closing stock indices extending from 1st October 2019 to 30th June 2020. The result reveals that there is short-run volatility in sample stock return except Australia and South Korea. Similarly, there is long-term volatility persistence in sample countries’ stock exchange except Australia, Saudi Arabia, Russia, and France. However, Australia is only the country where there is no short- and long-run information transmission derived from China. Therefore, there is a portfolio diversification opportunity in this country during COVID-19. Overall, this paper shows significant interdependencies between the Chinese and the G20 markets which furnish momentous implications to the stakeholders of markets.

In this paper, a dynamic interbank market network model based on bank agent behaviors is developed to analyze financial contagion with counter-party and liquidity channels. Afterwards, we analyze the impact of dynamics on the stability of interbank market and find that dynamics of interbank market could enhance the resilience of the network, which suggests contagion might be overestimated in current studies. Moreover, we investigate the mechanism of contagion when counter-party and liquidity channels are both active in the dynamic interbank market network. Specifically, we analyze the effects of bank capitalization, interbank exposures, liquid assets, and bank credit lending preference on the stability of the banking system, respectively. First, we find that liquidity in interbank market and fluctuations of deposits could amplify the negative impact of each other on the resilience of interbank market network. Second, banks with higher capitalization level tend to be more resilient against financial contagion. Third, interbank exposures may have multiple effects on the resilience of interbank market network. Fourth, the resilience of interbank market network is a nonmonotonic function of percentage of liquid assets. Finally, we discover a complex relationship between bank credit lending preference and the resilience of interbank market.

This paper explores a simulation framework to examine the financial contagion on the creditee linkage networks, owing to a comprehensive loan-level database of China Banking Regulatory Commission (CBRC). We merge two kinds of connections together, i.e., loan guarantee and shareholding relationships, when constructing our underlying network. Default transmission probabilities are characterized by the two-parameter logistic function with a variant of leverage. Our main finding is that, under the above channel, contagion spreads in a linear form. To be precise, as the scale of the initial shock grows, the total number of default firms and the amount of bad loans incurred both increase linearly. The main reason for this is that the underlying network is rather sparse. Within the same ratio of initially default companies, the Manufacturing Industry is likely to cause the largest number of companies to fail and the largest amount of bad loans. We also find that the default contagion mostly spreads within the same industries, explaining again why the domino effect is very limited. We investigate the impacts on different regions from industry failures, and get many interesting findings, e.g., most industries may influence the Yangtze Delta Economic Circle significantly except for Agriculture and the Mining Industry.