scholarly journals S-shaped transition trajectory and dynamic development frontier of the financial systemic risk research: a multiple networks analysis

Author(s):  
Wei Zhou ◽  
Ning Chen
2021 ◽  
Vol 50 (2) ◽  
pp. 74-95
Author(s):  
Yu.S. Evlakhova ◽  
◽  
E.N. Alifanova ◽  
A.A. Tregubova ◽  
◽  
...  

This paper finds out the behavior patterns of the Russian banking sector and systemically important banks in response to changes in the population financial activity under the economic shocks. The results show that the Russian banking sector has a behavior pattern that includes the sequence of actions: the outflow of deposits — vulnerability to non-repayment of loans — deposit bubble — credit bubble. We find no consistent evidence that systemically important banks show the same sequence of actions during the crises. We also find that the banking sector behavior and systemically important banks’ behavior varied in 2008–2009, but became the same in the crisis of 2014–2015. The coincidence of behavior patterns of the banking sector and systemically important banks increases the systemic risk. Research on intragroup differences between systemically important banks will allow finding solutions to reduce the risk.


Cybergeo ◽  
2011 ◽  
Author(s):  
César Ducruet ◽  
Daniele Ietri ◽  
Céline Rozenblat

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 711
Author(s):  
Xiao Bai ◽  
Huaping Sun ◽  
Shibao Lu ◽  
Farhad Taghizadeh-Hesary

The Covid-19 pandemic has brought about a heavy impact on the world economy, which arouses growing concerns about potential systemic risk, taking place in countries and regions. At this critical moment, it makes sense to interpret the systemic risk from the perspective of the financial crisis framework. By combing the latest research on systemic risks, we may arrive at some precautions relating to the current events. This literature review verifies the origin of systemic risk research. By comparing the retrieved and screened systemic literature with the relevant research on the financial crisis, more focus on the micro-foundations of systemic risk has been discovered. Besides, the measurement methods of systemic risks and the introduction of interdisciplinary methods have made the research in this field particularly active. This paper synthesizes the previous research conclusions to find the appropriate definition of systemic risk and combs the research literature of systemic risk from two lines: Firstly, conducting the division according to the sub-branch fields within the financial discipline and the relevant interdisciplinary research methods, which is helpful for scholars within and outside the discipline to have a more systematic understanding of the research in this field. Secondly predicting the research direction that can be expanded in this field.


2019 ◽  
Vol 22 (02) ◽  
pp. 1950002 ◽  
Author(s):  
NADINE WALTERS ◽  
GUSTI VAN ZYL ◽  
CONRAD BEYERS

We consider the fraction of nodes that default in large, stochastic, inhomogeneous financial networks following an initial shock to the system. Results for deterministic sequences of networks are generalized to stochastic networks to account for interbank lending relationships that change frequently. A general class of inhomogeneous stochastic networks is proposed for use in systemic risk research, and we illustrate how results that hold for Erdős–Rényi networks can be generalized to the proposed network class. The network structure of a system is determined by interbank lending behavior which may vary according to the relative sizes of the banks. We then use the results of the paper to illustrate how network structure influences the systemic risk inherent in large banking systems.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.


1986 ◽  
Vol 31 (7) ◽  
pp. 498-500
Author(s):  
Daniel R. Hanson
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document