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Standard market risk optimization tools, based on assumptions of normality, are ineffective for evaluating credit risk. In this article, the authors develop three scenario optimization models for portfolio credit risk. They first create the trading risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk‐return efficient frontier is constructed using parametric programming. While scenario optimization of quantile‐based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. The three models are applied to optimizing the risk‐return profile of a portfolio of emerging market bonds.

In recent years, several methodologies for measuring portfolio credit risk have been introduced that demonstrate the benefits of using internal models to measure credit risk in the loan book. These models measure economic credit capital and are specifically designed to capture portfolio effects and account for obligor default correlations. An example of an integrated market and credit risk model that overcomes this limitation is given in Iscoe et al. [1999], which is equally applicable to commercial and retail credit portfolios. However, the measurement of portfolio credit risk in retail loan portfolios has received much less attention than the commercial credit markets. This article proposes a methodology for measuring the credit risk of a retail portfolio, based on the general portfolio credit risk framework of Iscoe et al. The authors discuss the practical estimation and implementation of the model. They demonstrate its applicability with a case study based on the credit card portfolio of a North American financial institution. They also analyze the sensitivity of the results to various assumptions.

The IRB capital requirements of Basel II define the minimum level of capital that the bank has to retain to cover the current risks of its portfolio. The major risk that many banks are facing is credit risk and Basel II provides an approach to calculate its capital requirement. It is well known that Pillar I Basel II approach for credit risk capital requirements does not include concentration risk. The paper aims to propose a model modifying Basel II methodology (IRB) to include name concentration risk.

The model is developed on data based on a portfolio of Greek companies that are financed by Greek commercial banks. Based on the initial portfolio, new portfolios were simulated having a range of different credit risk parameters. Subsequently, the credit VaR of various portfolios was regressed against the credit risk indicators such as Basel II capital requirements, modified Herfindahl Index and a non-linear model was developed. This model modifies the Pillar I IRB capital requirements model of Basel II to include name concentration risk.

As the Pillar I IRB capital requirements model of Basel II does not include concentration risk, the credit VaR calculations performed in the present work appeared to have gaps with the Basel II capital requirements. These gaps were more apparent when there was high concentration risk in the credit portfolios. The new model bridges this gap providing with a correction coefficient.

The credit VaR of a loan portfolio could be calculated from the bank easily, without the use of additional complicated algorithms and systems.

The model is constructed in such a way as to provide an approximation of credit VaR satisfactory for business loan portfolios whose risk parameters lie within the range of those in a realistic bank credit portfolio and without the application of Monte Carlo simulations.

The risk/return trade‐off has been a central tenet of portfolio management since the seminal work of Markowitz [1952]. The basic premise, that higher (expected) returns can only be achieved at the expense of greater risk, leads naturally to the concept of an efficient frontier. The efficient frontier defines the maximum return that can be achieved for a given level of risk or, alternatively, the minimum risk that must be incurred to earn a given return. Traditionally, market risk has been measured by the variance (or standard deviation) of portfolio returns, and this measure is now widely used for credit risk management as well. For example, in the popular Credit‐Metrics methodology (J.P. Morgan [1997]), the standard deviation of credit losses is used to compute the marginal risk and risk contribution of an obligor. Kealhofer [1998] also uses standard deviation to measure the marginal risk and, further, discusses the application of mean‐variance optimization to compute efficient portfolios. While this is reasonable when the distribution of gains and losses is normal, variance is an inappropriate measure of risk for the highly skewed, fat‐tailed distributions characteristic of portfolios that incur credit risk. In this case, quantile‐based measures that focus on the tail of the loss distribution more accurately capture the risk of the portfolio. In this article, we construct credit risk efficient frontiers for a portfolio of bonds issued in emerging markets, using not only the variance but also quantile‐based risk measures such as expected shortfall, maximum (percentile) losses, and unexpected (percentile) losses.

I. INTRODUCTION A typical shortcoming of most current credit portfolio models is the lack of a stochastic modeling of risk factors, such as interest rates or credit spreads, during the revaluation process at the risk horizon. For example, fixed income instruments, such as bonds or loans, are revalued at the risk horizon using the current forward rates and (rating class specific) forward credit spreads for discounting future cash flows. Hence, the stochastic nature of the instrument's value in the future which results from changes in factors other than credit quality is ignored, and the riskiness of the credit portfolio at the risk horizon is underestimated. A further consequence is that correlations between changes of the debtor's default probability and changes of market risk factors and, hence, the exposure at default cannot be integrated into the credit portfolio model. This drawback is especially relevant for portfolios of defaultable market‐driven derivatives. One reason why risk factors not directly related to credit risk are neglected in most current credit portfolio models is that there is still no commonly accepted approach for modeling the credit quality of a debtor and the dependencies between the credit quality changes of different debtors. Hence, it might be over‐ambitious to incorporate correlations between market risk factors and credit quality changes. Even empirical evidence on the sign of the correlation remains inconclusive. Additionally, introducing stochastic market risk factors and modeling the correlation between these risk factors and credit quality changes would significantly increase the computational burden for calculating robust risk measures of credit portfolios.

Tails probabilities are of paramount importance in shaping the risk profile of portfolios with credit risk sensitive securities. In this context, risk management tools require simulations that accurately capture the tails, and optimization models that limit tail effects. Ignoring tail events in the simulation or using inadequate optimization metrics can have significant effects and reduce portfolio efficiency. The resulting portfolio risk profile can be grossly misrepresented when long‐run performance is optimized without accounting for short‐term tail effects. This article illustrates pitfalls and suggests models to avoid them.

This paper aims to improve decision making in credit portfolio management through analytical data-mining methods, which should be used as data availability and data quality of credit portfolios increase due to (semi-)automated credit decisions, improved data warehouses and heightened information needs of portfolio management.

To contribute to this fact, this paper elaborates credit portfolio analysis based on cluster analysis. This statistical method, so far mainly used in other disciplines, is able to determine “hidden” patterns within a data set by examining data similarities.

Based on several real-world credit portfolio data sets provided by a financial institution, the authors find that cluster analysis is a suitable method to determine numerous multivariate contract specifications implying high or, respectively, low profit potential.

Nevertheless, cluster analysis is a statistical method with multiple possible settings that have to be adjusted manually. Thus, various different results are possible, and as cluster analysis is an application of unsupervised learning, a validation of the results is hardly possible.

By applying this approach in credit portfolio management, companies are able to utilize the information gained when making future credit portfolio decisions and, consequently, increase their profit.

The paper at hand provides a unique structured approach on how to perform a multivariate cluster analysis of a credit portfolio by considering risk and return simultaneously. In this context, this procedure serves as a guidance on how to conduct a cluster analysis of a credit portfolio including advices for the settings of the analysis.

Commodity price volatility and small variations in climate conditions may have an important impact on the creditworthiness of any agricultural project. The evolution of such risk factors is vital for the credit risk analysis of a rural bank. The purpose of this paper is to determine the importance of price volatility and climate factors within a default risk model.

The authors estimate a generalized linear model (GLM) based on a structural default risk model. With the estimated factor loadings, the authors simulate the loss distribution of the portfolio and perform stress test to determine the impact of the relevant risk factors on economic capital.

The results indicate that both the price volatility and climate factors are statistically significant; however, their economic significance is smaller compare to other factors that the authors control for: macroeconomic conditions for the agricultural sector and intermediate input prices.

The analysis of non-systemic risk factors such as price volatility and climate conditions requires statistical methods focussed on measuring causal effects at higher quantiles, not just at the conditional mean, this is, however, a current limitation of GLMs.

The authors provide a design of a portfolio credit risk model, that is more suited to the special characteristics of a rural bank, than commercial credit risk models.

The paper incorporates agricultural-specific risk factors in a default risk model and a portfolio credit risk model.

The purpose of this paper is to test whether diversification of credit portfolios across economic sectors leads to improved profitability and reduced credit risks for Ghanaian banks that have been characterized by high non-performing loans in recent times (IMF, 2011).

Static and dynamic estimations, namely Prais-Winsten, fixed and random effect estimators, feasible generalized least squares as well as the system generalized methods of moments are employed on the annual data of 30 Ghanaian banks that operated between 2007 and 2014 to determine the effect of loan portfolio diversification on bank performance.

The study shows that loan portfolio diversification does not improve banks’ profitability nor does it reduce banks’ credit risks.

The study focuses on a single banking system in Africa largely as a result of data limitation.

The study emphasizes the need for banks to perform a careful assessment of the effects of their lending policies geared toward increased sectoral diversification on their monitoring efficiency and effectiveness. A further investment in loan screening and monitoring is necessary to minimize credit risks.

This study is the first to present empirical evidence on the effects of loan portfolio diversification on bank performance in an emerging banking market in Africa.

This study aims to render a fundamental assessment of the Basel II internal ratings-based (IRB) approach by taking readings of the Australian banking sector since the implementation of Basel II and comparing them with signals from macroeconomic indicators, financial statistics and external credit ratings. The IRB approach to capital adequacy for credit risk, which implements an asymptotic single risk factor (ASRF) model, plays an important role in protecting the Australian banking sector against insolvency.

Realisations of the single systematic risk factor, interpreted as describing the prevailing state of the Australian economy, are recovered from the ASRF model and compared with macroeconomic indicators. Similarly, estimates of distance-to-default, reflecting the capacity of the Australian banking sector to absorb credit losses, are recovered from the ASRF model and compared with financial statistics and external credit ratings. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-2009 was most acutely felt, the authors measure the impact of the crisis on the Australian banking sector.

Measurements from the ASRF model find general agreement with signals from macroeconomic indicators, financial statistics and external credit ratings. This leads to a favourable assessment of the ASRF model for the purposes of capital allocation, performance attribution and risk monitoring. The empirical analysis used in this paper reveals that the recent crisis imparted a mild stress on the Australian banking sector.

Given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, the authors cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment.

Access to internal bank data collected by the prudential regulator distinguishes this paper from other empirical studies on the IRB approach and financial crisis of 2007-2009. The authors are not the first to attempt to measure the effects of the recent crisis, but they believe that they are the first to do so using regulatory data.