Credit risk

About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.

Islamic financing and bank risks: The case of Malaysia

Abstract: We examine whether Islamic financing can explain three important bank risks in a country with a dual banking system: credit risk, interest-rate risk, and liquidity risk. Using Malaysian data, we find that commercial banks with Islamic financing have significantly lower credit and liquidity risks but significantly higher interest-rate risk than banks without Islamic financing. There is also evidence that bank size is significantly related to credit risk; the proportion of loan sales to total liabilities and bank size are significant determinants of interest-rate risk; and off-balance-sheet financing, the extent of securitization, loan volatility, bank capital, and bank size are statistically significantly related to liquidity risk. © 2005 Wiley Periodicals, Inc.

Classification methods applied to credit scoring: A systematic review and overall comparison

Abstract: The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper, therefore, aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis. The general results show the use and importance of the main techniques for credit rating, as well as some of the scientific paradigm changes throughout the years.

The effect of credit risk on bank and bank holding company bond yields: evidence from the post-fdicia period

Abstract: In this article we examine whether the federal safety net is viewed by the market as being extended beyond de jure deposits to other bank debt and even the debt of bank holding companies (BHCs). We extend previous research by focusing on the post-FDICIA period and by examining the risk-return relation of bonds issued directly by banks, not BHCs. Our results provide evidence that both bank and BHC bonds are priced by the secondary market in relation to their underlying credit risk, particularly for less capitalized issuers, suggesting that proposals requiring banks to issue subordinated debt may enhance market monitoring and discipline and be useful in supplementing regulatory discipline.

Modeling Operational Risk With Bayesian Networks

Abstract: Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real-time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested. INTRODUCTION Bayesian networks (BNs) have recently been explored as a potential tool for various risk management applications. Its main features of combining subjective opinion with observed data and modeling cause-and-effects make it especially well suited for investigating and capturing the workings of financial institutions. Although its usage has thus far been limited to specific areas (e.g., it has been used for credit risk scoring by banks) its application to wider enterprise risks is being increasingly documented, especially in the area of operational risk (OR). Chapter 14 of Alexander (2003) provides a brief introduction to modeling OR using BNs via a banking example. Marshall (2001), Cruz (2002), and Hoffman (2002) give brief overviews of BNs and where they fit into the whole framework of OR modeling. There is also an illustrative albeit high-level discussion on causal modeling using BNs via a banking example in King (1999). The main purpose of this article is to consider two aspects of the application of BNs to OR in greater detail than has so far appeared in the literature. These are the theory and techniques of model updating, and the subject of model assessment. OR takes place in a dynamic setting, with more information becoming available as time progresses. Hence, it is useful to update the models used for OR to take account of this flow of information. This is feasible within the setting of BNs and was briefly mentioned in King (1999); the section on "Updating the Probabilities With New Data" of this article gives more details of how this can be implemented. In any modeling exercise, it is essential to check that the model used provides a reasonable representation of the actual experience. Again, this is best done dynamically in the OR setting, as more information arrives; this is covered in the section on "Model Assessment" of this article. The article is set out as follows. In the section on "Changes to Supervisory Regimes as a Driver for Operational Risk Modeling," we describe recent developments in the supervision of financial institutions and how this has encouraged greater efforts in OR modeling. In the "Current Approaches to Modeling OR" section, we give a brief introduction to modeling approaches that have been used in the context of OR. The approach used in this article is that of BNs, which are introduced in the next section and applied in a general risk management context in the subsequent section. In the sections on "Updating the Probabilities With New Data" and "Model Assessment," we consider two aspects of BNs and OR that have not been covered in any detail in the literature to date: updating the models and monitoring the appropriateness of the model. …