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

2020 ◽  
Vol 45 (3) ◽  
pp. 1127-1152 ◽  
Author(s):  
Agostino Capponi ◽  
Xu Sun ◽  
David D. Yao
Keyword(s):  
Systemic Risk ◽  
Dynamic Network ◽  
Weak Limit ◽  
Capital Requirements ◽  
Stress Index ◽  
Financial Activity ◽  
Dynamic Network Model ◽  
Monetary Reserve ◽  
Independent Brownian Motion ◽  
Interbank Lending

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.

scholarly journals A dynamic network model of the unsecured interbank lending market

2018 ◽  
Vol 90 ◽  
pp. 310-342 ◽  
Author(s):  
Francisco Blasques ◽  
Falk Bräuning ◽  
Iman van Lelyveld
Keyword(s):  
Network Model ◽  
Dynamic Network ◽  
Dynamic Network Model ◽  
Interbank Lending

scholarly journals Systemic Risk and Insurance Regulation †

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Fabiana Gómez ◽  
Jorge Ponce
Keyword(s):  
Systemic Risk ◽  
Formal Model ◽  
Public Support ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Prudential Regulation ◽  
High Exposure ◽  
Insurance Firms ◽  
Low Exposure

This paper provides a rationale for the macro-prudential regulation of insurance companies, where capital requirements increase in their contribution to systemic risk. In the absence of systemic risk, the formal model in this paper predicts that optimal regulation may be implemented by capital regulation (similar to that observed in practice, e.g., Solvency II ) and by actuarially fair technical reserve. However, these instruments are not sufficient when insurance companies are exposed to systemic risk: prudential regulation should also add a systemic component to capital requirements that is non-decreasing in the firm’s exposure to systemic risk. Implementing the optimal policy implies separating insurance firms into two categories according to their exposure to systemic risk: those with relatively low exposure should be eligible for bailouts, while those with high exposure should not benefit from public support if a systemic event occurs.

scholarly journals Systemic risk measurement: bucketing global systemically important banks

2021 ◽  
Author(s):  
Marina Brogi ◽  
Valentina Lagasio ◽  
Luca Riccetti
Keyword(s):  
Systemic Risk ◽  
Financial System ◽  
Capital Requirements ◽  
Insurance Premium ◽  
Risk Measurement ◽  
Marginal Contribution ◽  
General Consensus ◽  
Systemically Important Banks

AbstractThe general consensus on the need to enhance the resilience of the financial system has led to the imposition of higher capital requirements for certain institutions, supposedly based on their contribution to systemic risk. Global Systemically Important Banks (G-SIBs) are divided into buckets based on their required additional capital buffers ranging from 1% to 3.5%. We measure the marginal contribution to systemic risk of 26 G-SIBs using the Distressed Insurance Premium methodology proposed by Huang et al. (J Bank Financ 33:2036–2049, 2009) and examine ranking consistency with that using the SRISK of Acharya et al. (Am Econ Rev 102:59–64, 2012). We then compare the bucketing using the two academic approaches and supervisory buckets. Because it leads to capital surcharges, bucketing should be consistent, irrespective of methodology. Instead, discrepancies in the allocation between buckets emerge and this suggests the complementary use of other methodologies.

The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges

2013 ◽  
Vol 44 (7) ◽  
pp. 1349-1360 ◽  
Author(s):  
M. Wichers
Keyword(s):  
Dynamic Network ◽  
Building Blocks ◽  
Major Depressive ◽  
Dynamic Network Model ◽  
Healthy State ◽  
Behavioural Patterns ◽  
Micro Level ◽  
Continuous Dynamic ◽  
Dynamic Interplay ◽  
Over Time

The examination of moment-to-moment, ‘micro-level’ patterns of experience and behaviour using experience sampling methodology has contributed to our understanding of the ‘macro-level’ development of full-blown symptoms and disorders. This paper argues that the micro-level perspective can be used to identify the smallest building blocks underlying the onset and course of mental ill-health. Psychopathology may be the result of the continuous dynamic interplay between micro-level moment-to-moment experiences and behavioural patterns over time. Reinforcing loops between momentary states may alter the course of mental health towards either a more or less healthy state. An example with observed data, from a population of individuals with depressive symptoms, supports the validity of a dynamic network model of psychopathology and shows that together and over time, this continuous interplay between momentary states may result in the cluster of symptoms we call major depressive disorder. This approach may help conceptualize the nature of mental disorders, and generate individualized insights useful for diagnosis and treatment in psychiatry.

Dynamic Network Model Analysis Based on Communication Network

2014 ◽  
Vol 989-994 ◽  
pp. 2639-2642
Author(s):  
Nan Qi Yuan ◽  
Tian Jiang ◽  
Shi Bai ◽  
Hao Sun ◽  
Jing Mei Zhao
Keyword(s):  
Communication Network ◽  
Convergence Rate ◽  
Network Model ◽  
Dynamic Network ◽  
Model Analysis ◽  
Vicsek Model ◽  
Dynamic Network Model ◽  
Weighted Model

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.

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