Cover image for Global credit review. Volume 3.
Global credit review. Volume 3.
ISBN:
9789814566148
Title:
Global credit review. Volume 3.
Physical Description:
1 online resource (175 pages) : illustrations
Contents:
Message from the Editor; Systemic Risk in Europe Eric Jondeau and Michael Rockinger; INTRODUCTION; I. HOW TO MEASURE SYSTEMIC RISK?; II. MODELLING SYSTEMIC RISK; III. THE SITUATION IN EUROPE; IV. THE SITUATION OF EUROPEAN INSTITUTIONS; NOTE; REFERENCES; Changes in the Ratings Game -- An Update on Various Developments RMI staff; INTRODUCTION; I.A CONSTRUCTIVE RESPONSE TO THE CRA CRITIQUES; 1.1. Litigation; II. LANDMARK CASE; III. CRA REGULATIONS; 3.1. United States; 3.2. Europe; IV. INTERNATIONAL RECOMMENDATIONS; V. IMPROVING CURRENT CRA REGULATIONS.

5.1. The US State Insurance RegulatorsVI. CONCLUDING REMARKS; NOTES; Reserve Requirements as Window Guidance in China Violaine Cousin; INTRODUCTION; I. RESERVE REQUIREMENTS -- AN OVERVIEW; 1.1. Reserve Requirements as Monetary Policy Tool; 1.2. Reserve Requirements in China; 1.3. Excess Reserves in China; 1.4. Impact of RRR Changes on Banks; II. RESEARCH DESIGN AND METHODOLOGY; 2.1. Data Set Development; 2.2. Descriptive Statistics; 2.3. Outliers Analysis; 2.4. Rationale for Using MM-Estimates Robust Regression; III. ROBUST REGRESSION RESULTS; 3.1. Overall Impact on Loan Quality.

3.2. Results Based on Different State Links3.3. Impact Under Different Conditions; 3.4. Impact of Excess Reserves; IV. CONCLUSION; NOTES; REFERENCES; APPENDIX A; APPENDIX B; APPENDIX C; APPENDIX D; APPENDIX E; The Implementation of the Basel II Default Definition by Credit Risk Assessment Systems: An Analysis of Possible Aggregation Procedures Markus Bingmer and Laura Auria; INTRODUCTION; I. THE TASK OF AGGREGATING DIFFERENT DEFAULT REPORTS; II. A THEORETICAL ANALYSIS OF THE AGGREGATION TASK; 2.1. Using the Default Information of a Single Bank; 2.2. Considering All Defaults.

2.3. Building a Default Indicator Based on the Binomial Distribution with the Goal of Consistency2.4. Considering Materiality of Defaults; III. AN EMPIRICAL ANALYSIS OF AGGREGATION WITH THE MATERIALITY THRESHOLD; IV. CONCLUSION; NOTES; REFERENCES; Can Credit-Scoring Models Effectively Predict Microloans Default? Statistical Evidence from the Tunisian Microfinance Bank Ibtissem Baklouti and Abdelfettah Bouri; INTRODUCTION; I. CREDIT SCORING IN MICROFINANCE INSTITUTIONS: THE LITERATURE; II. DATA AND MODEL; 2.1. Sample Selection and Variables Identifi cation; 2.2. Model Description.

III. EMPIRICAL RESULTS3.1. Univariate Analysis; 3.2. Model Estimation; 3.3. Calibration; 3.3.1. Quality of the logistic regression model; 3.3.2. Validation of the credit-scoring model; IV. CONCLUSION; NOTES; REFERENCES; Stepping Up to the Liquidity Challenge: The Changing Role of Credit Portfolio Management IACPM and KPMG; INTRODUCTION; I. STEPPING UP TO THE LIQUIDITY CHALLENGE: THE CHANGING ROLE OF CPM; II. REGULATORY CHALLENGES; III. SIGNIFICANT CHALLENGES; IV. MANAGING LIQUIDITY RISK; V. MODELING LIQUIDITY RISK; VI. A CONTINUING JOURNEY.
Local Note:
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Format:
Electronic Resources
Electronic Access:
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Publication Date:
2014
Publication Information:
Singapore :

World Scientific,

[2014]

©2014