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Credit risk
About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.
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TL;DR: In this article, an investment accelerator model augmented with a cash flow variable was used on panel data for individual farms during 1996-2000 to confirm the presence of imperfections on the rural credit market in Poland in the second half of the transition period and to identify farms that were the most affected by these imperfections.
Abstract: The objective of this article is to confirm the presence of imperfections on the rural credit market in Poland in the second half of the transition period, and to identify farms that were the most affected by these imperfections. For this, an investment accelerator model augmented with a cash flow variable was used on panel data for individual farms during 1996–2000. The cash flow coefficient was found to be significant and positive, indicating a poorly functioning rural credit market, in the sense that for some farms internal funds were the only source of funds (for farms facing credit rationing) or a less expensive source (for farms facing high borrowing costs) than debt. Farms facing more severe credit constraints were then identified by splitting the sample into two groups according to a single criterion but also by creating classes with a multiple component analysis. Farms less collateralisable were found to have experienced the most severe constraints. This finding is in line with other existing stu...
88 citations
TL;DR: Wang et al. as discussed by the authors used a two-step generalized method of moments system estimator to examine the impacts of risk, competition and cost efficiency on profitability of a sample of Chinese commercial banks over the period 2003-2013.
Abstract: This study aims to test the impacts of risk-taking behaviour, competition and cost efficiency on bank profitability in China.,A two-step generalized method of moments system estimator is used to examine the impacts of risk, competition and cost efficiency on profitability of a sample of Chinese commercial banks over the period 2003-2013.,The paper finds that credit risk, liquidity risk, capital risk, security risk and insolvency risk significantly influence the profitability of Chinese commercial banks. To be more specific, credit risk is significantly and negatively related to bank profitability; liquidity risk is significantly and positively related to return on assets (ROA) and net interest margin (NIM) but negatively related to return on equity (ROE); capital risk has a significant and negative impact on ROA and NIM but a positive impact on ROE; there is a significant and negative impact of security risk on bank profitability (ROA and NIM). It is found that Chinese commercial banks with higher levels of insolvency risk have higher profitability (ROA and ROE). Finally, higher competition leads to lower profitability in the Chinese banking industry, and Chinese commercial banks with higher levels of cost efficiency have lower ROA. In other words, the structure–conduct–performance paradigm rather than the efficient–structure paradigm holds in the Chinese banking industry.,This is the first paper to investigate the impact of different types of risk, including credit risk, liquidity risk, capital risk, security risk and insolvency risk, on bank profitability. This is the first study which uses more accurate measurements of efficiency and competition compared to previous Chinese banking profitability literature and which tests their impact on bank profitability. The findings not only provide a general picture on the risk, efficiency and competition conditions in the Chinese banking industry, but also give valuable information to the Chinese Government and to the banking regulatory authorities to make relevant policies.
88 citations
TL;DR: In this article, the authors analyzed macroeconomic and bank specific determinants of credit risk in Islamic and conventional banks and found that risky sector financing; regulatory capital (REGCAP) and Islamic Contract are significant to credit risk of Islamic banks.
Abstract: The study analyzes macroeconomic and bank specific determinants of credit risk in Islamic and Conventional Banks. Multivariate Regression analysis is applied on the sample of 15 conventional banks and 13 Islamic Banks in Malaysia over the period between 2000 and 2010. The finding shows that the banks specific determinants of credit risk are uniquely influenced the credit risk formation of Islamic and Conventional banks. The study found that risky sector financing; regulatory capital (REGCAP) and Islamic Contract are significant to credit risk of Islamic banks. For Conventional Banks, loan loss provision, debt-to-total asset ratio, REGCAP, size, earning management and Liquidity are significant factors influencing credit risk. As for macroeconomic factors only Inflation and M3 are significant to credit risk for both Islamic and Conventional banks.
87 citations
Book•
08 Nov 2010
TL;DR: The European Solvency II Project as discussed by the authors is a project of the CEIOPS Europe-Middle East Risk Management and the Enterprise (ESMER) project, which aims to address the challenges of enterprise risk management and solvency assessment systems.
Abstract: Solvency Introduction Solvency A Historical Review Managing Risks and the Enterprise A Summary of the Development of Enterprise Risk Management and Solvency Elements of Solvency Assessment Systems Valuation, Investments, and Capital Total Balance Sheet Approach Asset Valuation Liability Valuation Other Valuation Issues Investments and Own Funds Accounting Valuation Modeling and Measuring Developing a Model Dependence Risk Measures Capital Requirement: Modeling and Measuring Risks and Subrisks Market Risk Credit Risk Operational Risk Liquidity Risk Underwriting/Insurance Risk The European Solvency II General Ideas, Valuation, and Investment: Final Advice European Solvency II: General Ideas European Solvency II: Asset Valuation European Solvency II Project: Liability Valuation European Solvency II Project: Eligible Own Funds and Investments The European Solvency II Standard Formula: Final Advice Solvency II: The Standard Formula Framework Solvency II Standard Formula: Market Risk Solvency II Standard Formula: Credit Risk Solvency II Standard Formula: Operational Risk Solvency II Standard Formula: Liquidity Risk Solvency II Standard Formula: Nonlife Underwriting Risk Solvency II Standard Formula: Life Underwriting Risk Solvency II Standard Formula: Health Underwriting Risk Solvency II Standard Formula: Minimum Capital Requirement Backgrounds and Calibrations Some Statistical Clarifications Approximations for Skewness List of Different Papers That Has Been Published by CEIOPS European Solvency II Project European Solvency II: General Ideas European Solvency II: Asset Valuation European Solvency II: Liability Valuation European Solvency II: The Standard Formula Framework European Solvency II Standard Formula: Market Risk European Solvency II Standard Formula: Credit Risk European Solvency II Standard Formula: Operational Risk European Solvency II Standard Formula: Liquidity Risk European Solvency II Standard Formula: Nonlife Underwriting Risk European Solvency II Standard Formula: Life Underwriting Risk European Solvency II Standard Formula: Health Underwriting Risk European Solvency II Standard Formula: Minimum Capital Requirement References Index
87 citations
TL;DR: The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model, which is an important tool for assessing risks in financial industry.
Abstract: Highlights? The K-S and AUC values of the behavioral scoring model are 44.6% and 78.7%. ? The K-S and AUC values of the credit bureau scoring model are 51.0% and 80.9%. ? The K-S and AUC values of the dual scoring model are 59.3% and 87.7%. ? The dual scoring model exhibits greater predictive ability. Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank's internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.
87 citations