scholarly journals Network centrality measures and systemic risk: An application to the Turkish financial crisis

2014 ◽  
Vol 405 ◽  
pp. 203-215 ◽  
Author(s):  
Tolga Umut Kuzubaş ◽  
Inci Ömercikoğlu ◽  
Burak Saltoğlu
Keyword(s):  
Financial Crisis ◽  
Systemic Risk ◽  
Centrality Measures ◽  
Network Centrality

scholarly journals The Assessment of Systemic Risk in the Kenyan Banking Sector

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hong Fan ◽  
Allan Alvin Lee Lukaya Amalia ◽  
Qian Qian Gao
Keyword(s):  
Systemic Risk ◽  
Banking Sector ◽  
Stress Test ◽  
Banking System ◽  
Financial Data ◽  
Annual Reports ◽  
Centrality Measures ◽  
Network Centrality ◽  
Regulatory Body ◽  
Stress Tests

The present paper aims to assess the systemic risk of the Kenyan banking system. We propose a theoretical framework to reveal the time evolution of the systemic risk using sequences of financial data and use the framework to assess the systemic risk of the Kenyan banking system that is regarded as the largest in the East and Central African region. Firstly, we estimate the bilateral exposures matrix using aggregate financial data on loans and deposits from annual reports and analyze the interconnectedness in the market using network centrality measures. Next, we extend the Eisenberg–Noe method to a multiperiod setting to the systemic risk of the Kenyan banking system, in which the multiperiod includes the dynamic evolutions of the Kenyan banking system of every bank and the structure of the interbank network system. We apply this framework to assess dynamically the systemic risk of the Kenyan banking system between 2009 and 2015. The main findings are the following. The theoretical network analysis using network centrality measures showed several banks displaying characteristics of systematically important banks (SIBs). The theoretical default analysis showed that a bank suffering a basic default will trigger a contagious default that caused several other banks in the sector to go bankrupt. Further stress test proved that the KCB bank theoretically caused a few contagious defaults due to an unusually high interconnectedness. This methodology can contribute by being part of monitoring system of the Central Bank of Kenya (regulatory body) as well as the implementation of policies (such as bank-internal stress tests) that assist in preventing default contagion.

scholarly journals Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

2021 ◽  
Author(s):  
Sheri Markose ◽  
Simone Giansante ◽  
Nicolas A. Eterovic ◽  
Mateusz Gatkowski
Keyword(s):  
Early Warning ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Market Price ◽  
System Stability ◽  
Conditional Value At Risk ◽  
Centrality Measures ◽  
Global Banking ◽  
Pair Method

AbstractWe analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas.

Comparison and Application of Metrics That Define the Components Modularity in Complex Products

2008 ◽  
Author(s):  
Qi D. Van Eikema Hommes
Keyword(s):  
Social Network ◽  
Centrality Measures ◽  
Software Systems ◽  
Network Centrality ◽  
Good Pair ◽  
Centrality Metrics ◽  
The Social ◽  
Social Network Centrality ◽  
Sample Test

As the content and variety of technology increases in automobiles, the complexity of the system increases as well. Decomposing systems into modules is one of the ways to manage and reduce system complexity. This paper surveys and compares a number of state-of-art components modularity metrics, using 8 sample test systems. The metrics include Whitney Index (WI), Change Cost (CC), Singular value Modularity Index (SMI), Visibility-Dependency (VD) plot, and social network centrality measures (degree, distance, bridging). The investigation reveals that WI and CC form a good pair of metrics that can be used to assess component modularity of a system. The social network centrality metrics are useful in identifying areas of architecture improvements for a system. These metrics were further applied to two actual vehicle embedded software systems. The first system is going through an architecture transformation. The metrics from the old system revealed the need for the improvements. The second system was recently architected, and the metrics values showed the quality of the architecture as well as areas for further improvements.

scholarly journals Measuring the systemic risk in the South African banking sector

2017 ◽  
Vol 20 (1) ◽  
Author(s):  
Gregory M. Foggitt ◽  
Andre Heymans ◽  
Gary W. Van Vuuren ◽  
Anmar Pretorius
Keyword(s):  
Financial Crisis ◽  
South African ◽  
Systemic Risk ◽  
Banking Sector ◽  
Risk Index ◽  
Financial Sector ◽  
Ethical Behaviour ◽  
The South ◽  
Case Scenario ◽  
Worst Case

Background: In the aftermath of the sub-prime crisis, systemic risk has become a greater priority for regulators, with the National Treasury (2011) stating that regulators should proactively monitor changes in systemic risk.Aim: The aim is to quantify systemic risk as the capital shortfall an institution is likely to experience, conditional to the entire financial sector being undercapitalised.Setting: We measure the systemic risk index (SRISK) of the South African (SA) banking sector between 2001 and 2013.Methods: Systemic risk is measured with the SRISK.Results: Although the results indicated only moderate systemic risk in the SA financial sector over this period, there were significant spikes in the levels of systemic risk during periods of financial turmoil in other countries. Especially the stock market crash in 2002 and the subprime crisis in 2008. Based on our results, the largest contributor to systemic risk during quiet periods was Investec, the bank in our sample which had the lowest market capitalisation. However, during periods of financial turmoil, the contributions of other larger banks increased markedly.Conclusion: The implication of these spikes is that systemic risk levels may also be highly dependent on external economic factors, in addition to internal banking characteristics. The results indicate that the economic fundamentals of SA itself seem to have little effect on the amount of systemic risk present in the financial sector. A more significant relationship seems to exist with the stability of the financial sectors in foreign countries. The implication therefore is that complying with individual banking regulations, such as Basel, and corporate governance regulations promoting ethical behaviour, such as King III, may not be adequate. It is therefore proposed that banks should always have sufficient capital reserves in order to mitigate the effects of a financial crisis in a foreign country. The use of worst-case scenario analyses (such as those in this study) could aid in determining exactly how much capital banks could need in order to be considered sufficiently capitalised during a financial crisis, and therefore safe from systemic risk.

scholarly journals Identifying key papers within a journal via network centrality measures

2016 ◽  
Vol 107 (3) ◽  
pp. 1005-1020 ◽  
Author(s):  
Saikou Y. Diallo ◽  
Christopher J. Lynch ◽  
Ross Gore ◽  
Jose J. Padilla
Keyword(s):  
Centrality Measures ◽  
Network Centrality

scholarly journals Machine Learning in Network Centrality Measures

2019 ◽  
Vol 51 (5) ◽  
pp. 1-32 ◽  
Author(s):  
Felipe Grando ◽  
Lisandro Z. Granville ◽  
Luis C. Lamb
Keyword(s):  
Machine Learning ◽  
Centrality Measures ◽  
Network Centrality

scholarly journals Predicting dengue importation into Europe, using machine learning and model-agnostic methods

2019 ◽  
Author(s):  
Donald Salami ◽  
Carla Alexandra Sousa ◽  
Maria do Rosário Oliveira Martins ◽  
César Capinha
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Transport Network ◽  
Air Transport ◽  
Health Concern ◽  
Centrality Measures ◽  
Network Centrality

ABSTRACTThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation.Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale.Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions.We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

Systemic Risk in Banking after the Great Financial Crisis

2014 ◽  
Author(s):  
Philipp Hartmann ◽  
Olivier de Bandt ◽  
José Luis Peydró
Keyword(s):  
Financial Crisis ◽  
Systemic Risk
Sign in / Sign up

Export Citation Format

Share Document