This paper provides a credit risk quantification system for banks to estaminate the credit risk of loans to small and mediume nterprises(SMEs). As we know, the most difficult thing for banks to handle SME loans is whose financial reporting lacks transparency and no valuable reference. We use non-financial variables and employ the logisitic regression to develop the credit risk predict model. We concludet: first, when construct a SMEs credit rating system, non-financial factors should be seriously considered and adopted. Second, because of positioned different stage of firm life cycle, the credit rating model should be set up differently by different stage of firm. Third, SME loans should to make much of establishing ¡§relationship-based¡¨ in order to meet the various demands of risk management.

We examine recovery rates of defaulted bonds in the US corporate bond market, based on a complete set of traded prices and volumes. A study of the trading microstructure around various types of default events is provided. We document temporary price pressure with high trading volumes on the default day and the following 30 days, and low trading activity thereafter. Based on this analysis, we determine market-based recovery rates and quantify various liquidity measures. We study the relation between the recovery rates and these measures, considering additionally a comprehensive set of bond characteristics, firm fundamentals, and macroeconomic variables. (authors' abstract)

Magistro darbe yra suformuluoti Lietuvoje veikiančių bankų fizinių ir juridinių asmenų kreditavimo principai ir būdai, išanalizuoti ir susisteminti įvairių Lietuvos ir užsienio autorių teoriniai ir praktiniai bankų kredito valdymo metodai, pateikti populiariausi kredito rizikos valdymo metodai, įvertinti kreditavimo paslaugų analizei taikytini metodai. Išsamiai atlikta AB DnB NORD banko kreditavimo paslaugų analizė, bei numatytos kreditavimo prognozės. Patvirtinama autorių suformuluota mokslinio tyrimo hipotezė, kad AB DnB NORD banko kredito rizikos valdymas turi būti gerinamas, taikant įvairesnius kredito rizikos valdymo metodus modelius. / This master’s final paper is formulate Lithuania banks operating in the natural and legal persons of credit principles and techniques to analyze and structure the various Lithuanian and foreign authors of theoretical and practical approaches to the management of bank credit, to provide the most popular credit risk management techniques, to assess the credit service analysis methods. In detail by the AB DnB NORD bank credit analysis services, and forecasts for the credit. Confirmed by the authors formulated the research hypothesis, that of AB DnB NORD bank credit risk management should be improved through the diversification of credit risk management methods of the models.

This dissertation investigates agency problems within risk transfer contracts. We pay particular attention to the consequences of credit risk transfer in the context of banking. The first two chapters provide an introduction and literature review. We then analyze the effect of counterparty risk on financial insurance contracts in the following two chapters, and uncover a new moral hazard problem on the part of the insurer. If the insurer believes it is unlikely that a claim will be made, it is advantageous for them to invest in assets which earn higher returns, but may not be readily available if needed. We find that counterparty risk can create an incentive for the insured to reveal superior information about the risk of their "investment". In particular, a unique separating equilibrium may exist even in the absence of any signalling device. This constitutes a first example in which the separation of types can be achieved without a costly signalling device. Our research suggests that regulators should be wary of risk being offloaded to other, possibly unstable parties, especially in financial markets such as that of credit derivatives. The fifth chapter models loan sales and loan insurance (e.g. credit default swaps) as two key instruments of risk transfer within the banking environment. Recent empirical evidence suggests that the asymmetric information problem is as relevant in loan insurance as it is in loan sales. Contrary to previous literature, this paper allows for informational asymmetries in both markets. Our results show that a well capitalized bank will tend to use loan insurance regardless of loan quality in the presence of moral hazard and relationship banking costs of loan sales. Finally, we show that a poorly capitalized bank may be forced into the loan sales market, even in the presence of possibly significant moral hazard and relationship banking costs that can depress the selling price. / Thesis (Ph.D, Economics) -- Queen's University, 2008-09-26 13:03:32.81

Banks today are the largest financial institutions around the world, with branches and subsidiaries throughout everyone’s life. However, commercial banks are facing risks when they are operating. Credit risk is one of the most significant risks that banks face, considering that granting credit is one of the main sources of income in commercial banks. Therefore, the management of the risk related to that credit affects the profitability of the banks. The aim of the research is to provide stakeholders with accurate information regarding the credit risk management of commercial banks with its impact on profitability.   The main purpose of the research is to investigate if there is a relationship between credit risk management and profitability of commercial banks in Europe. We also aim to investigate if the relationship is stable or fluctuating. In the research model, ROE and ROA are defined as proxies of profitability while NPLR and CAR are defined as proxies of credit risk management. The research collects data from the largest 47 commercial banks in Europe from 2007 to 2012 and formulates four hypotheses which are related to the research question. A series of statistical tests are performed in order to test if the relationship exists. Other statistical tests are performed to investigate if the relationship is stable or not.   The findings reveal that credit risk management does have positive effects on profitability of commercial banks. Between the two proxies of credit risk management, NPLR has a significant effect on the both ROE and ROA while CAR has an insignificant effect on both ROE and ROA. However, from 2007 to 2012, the relationships between all the proxies are not stable but fluctuating.

The dissertation deals with credit risk and default prediction for software companies in the light of Basel II, the new capital accord for financial institutions. A credit risk model was developed which can be used by lenders to predict the default of software companies. Such model was developed by using three independent approaches: In a first approach, a model was created which was based solely on quantitative data (i.e. accounting data). In a second approach, a model was developed which was based entirely on qualitative information, including management skills, know how, quality of services and others. In a third approach, the quantitative and the qualitative models were combined. The results indicate that a credit risk model which is based on both quantitative and qualitative information yields the strongest predictive power. (author´s abstract)

The aim of this study is to evaluate the risk-return efficiency of credit policies for managing portfolio credit risk of banking institutions. The focus of the empirical analysis is on the impact of risk pricing and problem loan restructuring on bank risk and returns using a simulation model that represents an operating environment of lenders servicing the Australian farm sector. Insurance theory principles and agency relationships between a borrower and a lender are integrated into the portfolio theory framework. The portfolio theory framework is then couched in terms of the capital budgeting approach to generate a portfolio return distribution function for a particular credit policy regime. Borrowers are segmented by region, industry, loan maturity and credit risk class. Each credit risk class defines risk constraints on which a stochastic simulation model may be developed for credit scoring an average borrower in a portfolio segment. The stochastic simulation method is then used to generate loan security returns for a particular credit policy regime through time with loan return outcomes weighted by the number of borrowers in a segment to give measures of portfolio performance. Stochastic dominance efficiency criteria are used to choose between distributions of NPV of bank returns measured for a number of credit policy alternatives. The findings suggest that banks servicing the Australian farm sector will earn more profit without additional portfolio risk if the maximum limit to which pricing accounts for default risk in loan reviews is positively linked to volatility of gross incomes of farm business borrowers. Importantly, credit-underwriting standards must also be formulated so as to procure farm business borrowers of above average productivity with loans that are fully secured using fixed assets. The results of simulations also suggest that restructuring loans in event of borrower default provide for large benefits compared to a �no restructuring� option.

Thesis (Ph.D.)--University of Waikato, 2008. / Title from PDF cover (viewed May 27, 2008) Includes bibliographical references (p. 313-326)

>Magister Scientiae - MSc / The first two chapters give the background, history and overview of the dissertation, together with the necessary mathematical preliminaries. Thereafter, the next four chapters deal with credit risk and credit derivatives.The final part of the dissertation is devoted to the Basel II bank regulatory framework and the mathematical modeling of asset allocation in bank management, pertaining to credit risk.Credit risk models can be categorized into two groups known as structural models and reduced form models. These models are used in pricing and hedging credit risk. In this thesis we review a variety of credit risk instruments described by models of the said types. One of the strategies utilized by companies to mitigate credit risk is by using credit derivatives.In this thesis, five main types of risk derivatives have been considered: credit swaps, credit linked notes, credit spreads, total return swaps and collaterized debt obligations. Valuation models for the first three derivatives that are mentioned above, are also presented in this dissertation.The material presented include some of the most recent developments in the literature. Our methods range from single-period modeling to application of stochastic optimal control theory. We expand on the material presented from the literature by way of simplifying or clarifying proofs, and by adding illustrative examples in the form of calculations, tables and simulations.Also, the entire Chapter 6 is a new original contribution to the existing literature on mathematical modeling of credit risk. Key words: credit risk; default risk; structural approach; reduced form approach; incomplete information approach; investment strategy; Basel II regulatory framework

Credit scoring is very important process in banking industry during which each potential or current client is assigned credit score that in certain way expresses client's probability of default, i.e. failing to meet his or her obligations on time or in full amount. This is a cornerstone of credit risk management in banking industry. Traditionally, statistical models (such as logistic regression model) are used for credit scoring in practice. Despite many advantages of such approach, recent research shows many alternatives that are in some ways superior to those traditional models. This master thesis is focused on introducing ensemble learning models (in particular constructed by using bagging, boosting and stacking algorithms) with various base models (in particular logistic regression, random forest, support vector machines and artificial neural network) as possible alternatives and challengers to traditional statistical models used for credit scoring and compares their advantages and disadvantages. Accuracy and predictive power of those scoring models is examined using standard measures of accuracy and predictive power in credit scoring field (in particular GINI coefficient and LIFT coefficient) on a real world dataset and obtained results are presented. The main result of this comparative study is that...