Quality control for structural credit risk models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential — hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon–Nikodym derivative for a change of measure.     

Write-down bonds,credit risk and imperfect information

A structural model of pricing Write-Down (hereafter WD) bonds under imperfect information has been developed to investigate the effect of WD bonds issuance on credit risk. Information is not only delayed but also asymmetrically distributed between managers and outside investors. We derive analytical solutions for corporate securities prices and find the issuance of WD bonds could significantly improve firm value via reducing bankruptcy cost. Our numerical results further demonstrate that the WD bonds issuance increases corporate risk tolerance and reduces the risk of bankruptcy and credit spreads under imperfect information.     

Default-risky bond prices with jumps,liquidity risk and incomplete information

This paper provides a default-risky bond valuation model, which assumes that the issuer’s credit quality, modelled by the intensity of default, is driven by a continuous-time Markov chain. The model accounts for default and liquidity risk as well as incomplete information. A full-information semimartingale representation of a liquid defaultable bond price, which separates three different types of risks—default, interest-rate and credit-quality, is obtained. The illiquidity is modelled as exogenously specified stochastic reduction in the price of the bond, which adds more risks for the investors. A model of a market with partially informed investors, belonging to specific investor classes and having access to discrete information sets about credit quality, was specified. Valuations of defaultable bonds in this market were provided as well as price impacts of the new information releases.      

Improved hybrid resampling and ensemble model for imbalance learning and credit evaluation

A clustering-based undersampling (CUS) and distance-based near-miss method are widely used in current imbalanced learning algorithms, but this method has certain drawbacks. In particular, the CUS does not consider the influence of the distance factor on the majority of instances, and the near-miss method omits the inter-class(es) within the majority of samples. To overcome these drawbacks, this study proposes an undersampling method combining distance measurement and majority class clustering. Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model (CDEILM). This algorithm combines distance-based undersampling, feature selection, and ensemble learning. In addition, a cluster size-based resampling (CSBR) method is proposed for preserving the original distribution of the majority class, and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods. The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework. The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods, and that the hybrid model provides the best results under most circumstances. Therefore, the proposed model can be used as an alternative imbalanced learning method under specific circumstances, e.g., for providing a solution to credit evaluation problems in financial applications.     

Cluster Analysis for mixed data: An application to credit risk evaluation

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.     

Holistic investment assessment: optimization,risk appraisal and decision making

On deciding for the most appropriate investment when capital restrictions exist, investors define their alternatives and analyze each one of them. Traditionally, the definition, appraisal and analysis stages are treated separately. Herein, an innovative holistic method is proposed for bridging these stages. Within this method, investment attributes definition occurs by genetic algorithm optimization, while the analysis of the investment is realized through simulation. The method also proposes the NPV Expected Shortfall and the NPV Risk Preference Index as investment evaluation criteria. An illustrative case study of two mutually exclusive renewable energy investment scenarios is also used for demonstration purposes. Copyright © 2008 John Wiley & Sons, Ltd.     

电网信息系统效能评估模型构建浅析

目前,国内外针对信息系统效能的研究还不多。文章在实践基础上,从信息系统效能的时间成本、经济成本和价值体系等角度出发,初步探析了电网信息系统效能评估的意义、主要工作阶段与内容,并介绍了如何基于DE Lone&McLean模型来架建电网信息系统效能评估模型。     

Use of a Markov decision process model for treatment selection in an asymptomatic disease with consideration of risk sensitivity

Some potentially dangerous diseases are completely asymptomatic. Their diagnosis as incidental findings of ever-more-sensitive medical imaging can leave patients and physicians in something of a quandary. The patient feels well, and potential interventions to stave off long-term deterioration or death bring with them immediate risks. We discuss the use of a Markov Decision Process (MDP) model (rather than Monte Carlo simulation of a Markov Model) to create a tool for analyzing individual treatment decisions for asymptomatic chronic diseases where a patient’s condition cannot improve. We formulate a finite-horizon MDP model to determine optimal treatment plans and discuss three distinct optimality criteria: (a) maximizing expected quality-adjusted-life years with and without discounting, (b) maximizing the expected number of life years in good health, and (c) maximizing the expected utility for number of years in good health. In (c) we assume exponential utility and consider different risk aversion factors reported in the medical literature. We illustrate the model’s use by considering asymptomatic intracranial aneurysm. Our model builds on a simulation model [19] created to examine treatment recommendations based on cost-effectiveness. We demonstrate that incorporating risk aversion leads to “no treatment” recommendations for some types of aneurysm. Furthermore, the use of alternate patient-selected criteria leads to recommendations that vary from [19] in several scenarios. We also discuss the use of the software as a decision support tool to help make individualized treatment recommendations and demonstrate that the computational performance of the algorithm makes its use feasible during a short office visit.     

Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.     

A resilience-based model for performance evaluation of information systems: the case of a gas company

Information systems (IS) are strongly influenced by changes in new technology and should react swiftly in response to external conditions. Resilience engineering is a new method that can enable these systems to absorb changes. In this study, a new framework is presented for performance evaluation of IS that includes DeLone and McLean’s factors of success in addition to resilience. Hence, this study is an attempt to evaluate the impact of resilience on IS by the proposed model in Iranian Gas Engineering and Development Company via the data obtained from questionnaires and Fuzzy Data Envelopment Analysis (FDEA) approach. First, FDEA model with α-cut = 0.05 was identified as the most suitable model to this application by performing all Banker, Charnes and Cooper and Charnes, Cooper and Rhodes models of and FDEA and selecting the appropriate model based on maximum mean efficiency. Then, the factors were ranked based on the results of sensitivity analysis, which showed resilience had a significantly higher impact on the proposed model relative to other factors. The results of this study were then verified by conducting the related ANOVA test. This is the first study that examines the impact of resilience on IS by statistical and mathematical approaches.