A Dynamic Network Model of Interbank Lending Systemic Risk and Liquidity Provisioning

2017 ◽  
Author(s):  
Agostino Capponi ◽  
Xu Sun ◽  
David D. Yao
2018 ◽  
Vol 90 ◽  
pp. 310-342 ◽  
Author(s):  
Francisco Blasques ◽  
Falk Bräuning ◽  
Iman van Lelyveld

2020 ◽  
Vol 45 (3) ◽  
pp. 1127-1152 ◽  
Author(s):  
Agostino Capponi ◽  
Xu Sun ◽  
David D. Yao

We develop a dynamic model of interbank borrowing and lending activities in which banks are organized into clusters, and adjust their monetary reserve levels to meet prescribed capital requirements. Each bank has its own initial monetary reserve level and faces idiosyncratic risks characterized by an independent Brownian motion, whereas system wide, the banks form a hierarchical structure of clusters. We model the interbank transactional dynamics through a set of interacting measure-valued processes. Each individual process describes the intracluster borrowing/lending activities, and the interactions among the processes capture the intercluster financial transactions. We establish the weak limit of the interacting measure-valued processes as the number of banks in the system grows large. We then use the weak limit to develop asymptotic approximations of two proposed macromeasures (the liquidity stress index and the concentration index), both capturing the dynamics of systemic risk. We use numerical examples to illustrate the applications of the asymptotics and conduct-related sensitivity analysis with respect to various indicators of financial activity.


2014 ◽  
Vol 989-994 ◽  
pp. 2639-2642
Author(s):  
Nan Qi Yuan ◽  
Tian Jiang ◽  
Shi Bai ◽  
Hao Sun ◽  
Jing Mei Zhao

In order to research dynamic network astringency reaching uniformity, this paper perfects the Vicsek model and puts forward improving dynamic network astringency efficiency by weighted model. We prove that the convergence rate of weighted model is faster than the classic Vicsek model and it can optimize dynamic network.


2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.


2015 ◽  
Vol 72 (5) ◽  
pp. 1153-1176 ◽  
Author(s):  
András Szabó-Solticzky ◽  
Luc Berthouze ◽  
Istvan Z. Kiss ◽  
Péter L. Simon

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