Banking stability, competition, and economic volatility

Fernández, Ana Isabel; González, Francisco; Suárez, Nuria

doi:10.1016/j.jfs.2016.01.005

Cited by 98 publications

(81 citation statements)

References 57 publications

(93 reference statements)

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“…Other insights were the relation between financial risk and economic development (Calmès & Theoret, 2014;Lupu, 2015;Fazio et al, 2015;Fernández et al, 2016).…”

Section: Quantitative Analysis In Financial Regulationmentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

255

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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“…Other insights were the relation between financial risk and economic development (Calmès & Theoret, 2014;Lupu, 2015;Fazio et al, 2015;Fernández et al, 2016).…”

Section: Quantitative Analysis In Financial Regulationmentioning

confidence: 99%

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Kou

Chao

Alsaadi

et al. 2019

Technological and Economic Development of Economy

255

122

View full text Add to dashboard Cite

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“…This measure has been widely used in literature of banking stability. Following (Clark et al, 2017); (Fernández et al, 2016); Rajhi and Hassairi (2013) this research uses Z-score to access the level of stability. This indicator is quite famous among the researchers for its precision.…”

Section: Measuring Stability Z-scorementioning

confidence: 99%

Evaluating Stability in Dual Banking System: Comparison Between Conventional and Islamic Banks in Malaysia

Alaeddin¹,

Khattak²,

Abojeib³

2019

HSSR

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Purpose of Study: This paper aims to explore whether Islamic banks are more stable when compared with conventional banks in a dual banking system. Methodology: This research employs Pooled OLS methodology for 42 banks, including 27 conventional banks and 15 Islamic banks, for the period of 2005-2016. Results: The study suggests that Islamic banks are less stable compared to conventional banks in overall banking sector. Furthermore, it is found that big Islamic banks are less stable than big conventional banks and small Islamic banks are less stable than small conventional banks. The results disapprove of the widespread belief that Islamic banks are more stable and more resilient to adverse shocks in the financial crisis. Moreover, while investigating the shift in overall level of banking stability with respect to financial crises, regardless of bank type and bank size, it is observed that the overall banking stability is enhanced after the financial crises. This is intriguing and a sigh of relief for policy makers and regulators in the country. Implications/Applications: This research is of contribution to policy makers and central banks in the countries with highly dual banking environment and for the central banks striving to become International Islamic financial hub.

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“…In bank loan contracts, non-price terms are essential components to reflect the riskiness of borrowing firms, mitigate the informational problems and specify the conditions when bank can intervene (Berlin and Mester, 1992;Rajan and Winton, 1995;Demiroglu and James, 2010;Inklaar et al, 2015;Fernandez et al, 2016). Typically, non-price terms in loan contracts include loan maturity, the use of collateral and various restrictive provisions (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Bank Market Power and Loan Contracts: Empirical Evidence

et al. 2017

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Using a sample of syndicated loan facilities granted to US corporate borrowers from 1987 to 2013, we directly gauge the lead banks’ market power, and test its effects on both price and non‐price terms in loan contracts. We find that bank market power is positively correlated with loan spreads, and the positive relation holds for both non‐relationship loans and relationship loans. In particular, we report that, for relationship loans, lending banks charge lower loan price for borrowing firms with lower switching cost. We further employ a framework accommodating the joint determination of loan contractual terms, and document that the lead banks’ market power is positively correlated with collateral and negatively correlated with loan maturity. In addition, we report a significant and negative relationship between banking power and the number of covenants in loan contracts, and the negative relationship is stronger for relationship loans.

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Banking stability, competition, and economic volatility

Cited by 98 publications

References 57 publications

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

Evaluating Stability in Dual Banking System: Comparison Between Conventional and Islamic Banks in Malaysia

Bank Market Power and Loan Contracts: Empirical Evidence

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