Heterogeneous market structure and systemic risk: Evidence from dual banking systems

Abedifar, Pejman; Giudici, Paolo; Hashem, Shatha Qamhieh

doi:10.1016/j.jfs.2017.11.002

Cited by 57 publications

(22 citation statements)

References 56 publications

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“…Some authors attempted to explore relations between different financial tools and systemic risk, such as credit derivative markets, hedge fund, capital network, Shadow Banking, and so on. Abedifar, Giudici, and Hashem (2017) found that the conventional banks with Islamic windows have the least resilient sector under systemic risk attack in dual banking systems. Calistru (2012) studied risk management measures for credit derivatives and suggested that the operational efficiency of credit derivative markets may prevent the occurrence of systemic risks.…”

Section: Financial Market Risk and Stability Analysismentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

254

122

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Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

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Section: Financial Market Risk and Stability Analysismentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

254

122

View full text Add to dashboard Cite

show abstract

“…Thus and although Islamic banks have the same governance structures, they are required to operate in a Shariah ‐compliant manner. This creates unique governance structures, as well as raises a new risk called “ Shariah risk” concerning the potential risk of becoming Shariah non‐compliant, which can generate a further financial turmoil and threaten Islamic banks' activities (e.g., cash deposits and withdrawals), and hence damage the banks' reputation (Abedifar, Giudici, & Hashem, 2017; Ashraf, Rizwan, & L'Huillier, 2016; Aysan & Ozturk, 2018; Bitar, Hassan, & Walker, 2017; Chapra & Ahmed, 2002; Grassa, 2015; Hassan & Aliyu, 2018; Safieddine, 2009). Further, Islamic banking has typically been operating with a weaker government oversight, which has led to a number of noticeable Islamic bank failures (e.g., Islas Finance House in Turkey, the Dubai Islamic Bank, the Islamic Investment Companies of Egypt) (Chapra & Ahmed, 2002; Grassa, 2015; Hasan, 2011; Safieddine, 2009).…”

Section: Bcrs Risk Disclosure and Governance Reforms In Mena Banksmentioning

confidence: 99%

Are bank risk disclosures informative? Evidence from debt markets

et al. 2020

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This study examines whether financial reporting with a specific focus on risk disclosures have a predictive (informative) effect on banks' credit ratings (BCRs) and, consequently, ascertains whether governance structures can moderate such an association. Using one of the largest bank‐level datasets collected from 12 Middle East and North African (MENA) countries over the 2006–2013 period to‐date, our findings are as follows. First, we find that risk disclosures have a predictive effect on BCRs. Second, we find that the relationship between risk disclosures and BCRs is contingent on the quality of governance structures. Specifically, we find that the informativeness of risk disclosures on BCRs is higher in banks with larger board size, greater independence, higher government ownership, and better Shariah supervisory board, but lower in banks with greater block ownership, higher foreign ownership and the presence of CEO duality. The central tenor of our findings remains unchanged after controlling for a number of firm‐ and country‐level factors, alternative risk disclosure measures, firm‐ and national‐level governance proxies, different types of banks, and potential endogeneities. The findings have important implications for investors, especially bondholders, standard‐setters, regulators, and central governments.

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“…The model presented in this study could be generalised to the study of contagion among other country specific financial indicators, or to the study of interconnectedness among other types of financial assets, with the limitation that the considered countries and/or assets should be kept constant over time. Examples of such extensions are contained in the papers by Giudici and Parisi (2017), who consider contagion netween country debt/gdp ratios Avdjev et al (2018), who consider contagion among country banking systems, Abedifar et al (2017), who considers contagion among different banking types, and finally Giudici and Abu-Hashish (2018), who consider interconnectedness among bitcoin prices and financial assets.…”

Section: Introductionmentioning

confidence: 99%

CoRisk: Credit Risk Contagion with Correlation Network Models

Giudici

Parisi

2018

Risks

Self Cite

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We propose a novel credit risk measurement model for Corporate Default Swap (CDS) spreads that combines vector autoregressive regression with correlation networks. We focus on the sovereign CDS spreads of a collection of countries that can be regarded as idiosyncratic measures of credit risk. We model CDS spreads by means of a structural vector autoregressive model, composed by a time dependent country specific component, and by a contemporaneous component that describes contagion effects among countries. To disentangle the two components, we employ correlation networks, derived from the correlation matrix between the reduced form residuals. The proposed model is applied to ten countries that are representative of the recent financial crisis: top borrowing/lending countries, and peripheral European countries. The empirical findings show that the contagion variable derived in this study can be considered as a network centrality measure. From an applied viewpoint, the results indicate that, in the last 10 years, contagion has induced a “clustering effect” between core and peripheral countries, with the two groups further diverging through, and because of, contagion propagation, thus creating a sort of diabolic loop extremely difficult to be reversed. Finally, the outcomes of the analysis confirm that core countries are importers of risk, as contagion increases their CDS spread, whereas peripheral countries are exporters of risk. Greece is an unfortunate exception, as its spreads seem to increase for both idiosyncratic factors and contagion effects.

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Heterogeneous market structure and systemic risk: Evidence from dual banking systems

Cited by 57 publications

References 56 publications

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Are bank risk disclosures informative? Evidence from debt markets

CoRisk: Credit Risk Contagion with Correlation Network Models

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