Optimal inventory and insurance decisions for a supply chain financing system with downside risk control

This research considers a supply chain financing system consisting of a capital‐constrained retailer, a supplier and a risk‐averse bank. The retailer may be subject to credit limit because of the bank's downside risk control, and hence, credit insurance should be needed to enhance his financing ability. This paper develops a mathematical optimization model by incorporating insurance policy into the well‐known newsvendor financing model. The optimal inventory and insurance decisions under different scenarios, that is, no insurance, insurance with symmetric information and insurance with asymmetric information, are derived. This work also discusses how the retailer's capital level, the bank's risk aversion, and the insurer's loading factor affect the optimal inventory and insurance decisions. The results show that the retailer will use credit insurance if he is sufficiently capital‐constrained or the insurer's risk loading factor is low enough. Moreover, credit insurance can bring Pareto improvement to the supply chain financing system, which verifies the prevalence of credit insurance in practice. Several numerical experiments are presented to examine the sensitivities of key parameters. Copyright © 2016 John Wiley & Sons, Ltd.     

The Information Content of Data in Defined Choice-Situations

This paper is concerned with what people's decisions imply about the usefulness of the data they receive. It does not deal with what might improve decision-making but with an attempt to understand what the measurement of information for decision-making involves. Operational definitions and measures of the information content of data are discussed. Ackoff and Emery's treatment of information as dependent on the choice-situation is followed, but a new concept, that of information inherent in the structure of the choice-situation is added. It is shown that the Shannon measure of uncertainty can be introduced into the Ackoff-Emery behavioural framework for dealing with measures of information. The greater clarity stemming from this partial unification of two apparently different approaches may help with the still formidable problems that are outlined.     

Cure events in default prediction

This paper evaluates the resurrection event regarding defaulted firms and incorporates observable cure events in the default prediction of SME. Due to the additional cure-related observable data, a completely new information set is applied to predict individual default and cure events. This is a new approach in credit risk that, to our knowledge, has not been followed yet. Different firm-specific and macroeconomic default and cure-event-influencing risk drivers are identified. The significant variables allow a firm-specific default risk evaluation combined with an individual risk reducing cure probability. The identification and incorporation of cure-relevant factors in the default risk framework enable lenders to support the complete resurrection of a firm in the case of its default and hence reduce the default risk itself. The estimations are developed with a database that contains 5930 mostly small and medium-sized German firms and a total of more than 23000 financial statements over a time horizon from January 2002 to December 2007. Due to the significant influence on the default risk probability as well as the bank’s possible profit prospects concerning a cured firm, it seems essential for risk management to incorporate the additional cure information into credit risk evaluation.     

Liquidity and credit risk

The paper uses fuzzy measure theory to represent liquidity risk, i.e. the case in which the probability measure used to price contingent claims is not known precisely. This theory enables one to account for different values of long and short positions. Liquidity risk is introduced by representing the upper and lower bound of the price of the contingent claim computed as the upper and lower Choquet integral with respect to a subadditive function. The use of a specific class of fuzzy measures, known as g λ measures enables one to easily extend the available asset pricing models to the case of illiquid markets. As the technique is particularly useful in corporate claims evaluation, a fuzzified version of Merton's model of credit risk is presented. Sensitivity analysis shows that both the level and the range (the difference between upper and lower bounds) of credit spreads are positively related to the ‘quasi debt to firm value ratio’ and to the volatility of the firm value. This finding may be read as correlation between credit risk and liquidity risk, a result which is particularly useful in concrete risk-management applications. The model is calibrated on investment grade credit spreads, and it is shown that this approach is able to reconcile the observed credit spreads with risk premia consistent with observed default rate. Default probability ranges, rather than point estimates, seem to play a major role in the determination of credit spreads.     

The benefit to consumers from generic scoring models based on credit reports

Received on 1 July 1991. The benefit to consumers from the use of informative creditreports is demonstrated by showing the improvement in creditdecisions when generic scoring models based on credit reportsare implemented. If these models are highly predictive, thenthe truncation of credit reports will reduce the predictivepower of bureau-based generic scoring systems. As a result,more good credit risks will be denied credit, and more poorcredit risks will be granted credit. It is shown that, evenwhen applied to credit applications that had already been screenedand approved, the use of generic scoring models significantlyimproves credit grantors' ability to predict and eliminate bankruptcies,charge-offs, and delinquencies. As applied to existing accounts,bureau-based generic scores are shown to have predictive valuefor at least 3 months, while scores 12 months old may not bevery powerful. Even though bureau-based scores shift towardsthe high-risk end of the distribution during a recession, theycontinue to rank risk very well. When coupled with application-basedcredit-scoring models, scores based on credit-bureau data furtherimprove the predictive power of the model-the improvements beinggreater with more complete bureau information. We conclude thatgovernment-imposed limits on credit information are anti-consumerby fostering more errors in credit decisions.     

Credit risk evaluation using multi-criteria optimization classifier with kernel,fuzzification and penalty factors

With the fast development of financial products and services, bank’s credit departments collected large amounts of data, which risk analysts use to build appropriate credit scoring models to evaluate an applicant’s credit risk accurately. One of these models is the Multi-Criteria Optimization Classifier (MCOC). By finding a trade-off between overlapping of different classes and total distance from input points to the decision boundary, MCOC can derive a decision function from distinct classes of training data and subsequently use this function to predict the class label of an unseen sample. In many real world applications, however, owing to noise, outliers, class imbalance, nonlinearly separable problems and other uncertainties in data, classification quality degenerates rapidly when using MCOC. In this paper, we propose a novel multi-criteria optimization classifier based on kernel, fuzzification, and penalty factors (KFP-MCOC): Firstly a kernel function is used to map input points into a high-dimensional feature space, then an appropriate fuzzy membership function is introduced to MCOC and associated with each data point in the feature space, and the unequal penalty factors are added to the input points of imbalanced classes. Thus, the effects of the aforementioned problems are reduced. Our experimental results of credit risk evaluation and their comparison with MCOC, support vector machines (SVM) and fuzzy SVM show that KFP-MCOC can enhance the separation of different applicants, the efficiency of credit risk scoring, and the generalization of predicting the credit rank of a new credit applicant.     

Lookahead scorecards for new fixed term credit products

Standard approaches to scorecard construction require that a body of data has already been collected for which the customers have known good/bad outcomes, so that scorecards can be built using this information. This requirement is not satisfied by new financial products. To overcome this lack, we describe a class of models based on using information about the length of time customers have been using the product, as well as any available information which does exist about true good/bad outcome classes. These models not only predict the probability that a new customer will go bad at some time during the product's term, but also evolve as new information becomes available. Particular choices of functional form in such models can lead to scorecards with very simple structures. The models are illustrated on a data set relating to loans.     

Subagging for credit scoring models

The logistic regression framework has been for long time the most used statistical method when assessing customer credit risk. Recently, a more pragmatic approach has been adopted, where the first issue is credit risk prediction, instead of explanation. In this context, several classification techniques have been shown to perform well on credit scoring, such as support vector machines among others. While the investigation of better classifiers is an important research topic, the specific methodology chosen in real world applications has to deal with the challenges arising from the real world data collected in the industry. Such data are often highly unbalanced, part of the information can be missing and some common hypotheses, such as the i.i.d. one, can be violated. In this paper we present a case study based on a sample of IBM Italian customers, which presents all the challenges mentioned above. The main objective is to build and validate robust models, able to handle missing information, class unbalancedness and non-iid data points. We define a missing data imputation method and propose the use of an ensemble classification technique, subagging, particularly suitable for highly unbalanced data, such as credit scoring data. Both the imputation and subagging steps are embedded in a customized cross-validation loop, which handles dependencies between different credit requests. The methodology has been applied using several classifiers (kernel support vector machines, nearest neighbors, decision trees, Adaboost) and their subagged versions. The use of subagging improves the performance of the base classifier and we will show that subagging decision trees achieve better performance, still keeping the model simple and reasonably interpretable.     

基于分类回归树的个人信用客观评分指标体系(英文)

本文提出了一种客观的个人信用指标体系.首先利用分类回归树量化每个指标对信用状况的影响程度,并以此量化值为每个指标设置不同的评分权重;然后通过定义风险度量值来确定指标中各个取值的评分,进而建立了新的评估指标体系.通过选取现实样本数据对指标体系做了实证分析,分析结果表明,新建的指标体系能很好地对借款人进行风险评价.     

Re-assignment and consolidation of credit authorizations

When a customer is granted several credit lines with different risk levels, the bank usually stipulates an authorization for each credit line and a total authorization; moreover authorizations are sometimes given for sets of credit lines. The purpose of the paper is, given a set of credit lines with corresponding authorizations, and a customer's current credit utilizations, to find new utilizations of greatest risk to the bank, within the credit authorization limits and with regard to the current utilizations. These new utilizations, used as re-assigned authorizations enable the bank to assess the risks relative to residual commitment and possible overstepping for each credit line. Furthermore these risks can be aggregated over a set of customers for each credit line. An algorithm has been developed to compute these re-assigned authorizations; it is based on classical linear programming methods. This paper describes this algorithm and recommends its use to consolidate risks.     

Effects of risk aversion and decision preference on equilibriums in supply chain finance incorporating bank credit with credit guarantee

We constructed a Stackelberg game in a supply chain finance (SCF) system including a manufacturer, a capital‐constrained retailer, and a bank that provides loans on the basis of the manufacturer's credit guarantee. To emphasize the financial service providers' risks, we assumed that both the bank and the manufacturer are risk‐averse and formulated trade‐off objective functions for both of them as the convex combination of the expected profit and conditional value‐at‐risk. To explore the effects of the risk preferences and decision preferences on SCF equilibriums, we mathematically analyzed the optimal order quantities, wholesale prices, and interest rates under different risk preference scenarios and performed numerical analyses to quantify the effects. We found that incorporating bank credit with a credit guarantee can effectively balance the retailer's financing risk between the bank and the manufacturer through interest rate charging and wholesale pricing. Moreover, SCF equilibriums with risk aversion are highly affected by the degree of both the lender's and guarantor's risk tolerance in regard to the borrower's default probability and will be more conservative than those in the risk‐neutral cases that only maximize expected profit.     

Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data

The 2004 Basel II Accord has pointed out the benefits of credit risk management through internal models using internal data to estimate risk components: probability of default (PD), loss given default, exposure at default and maturity. Internal data are the primary data source for PD estimates; banks are permitted to use statistical default prediction models to estimate the borrowers’ PD, subject to some requirements concerning accuracy, completeness and appropriateness of data. However, in practice, internal records are usually incomplete or do not contain adequate history to estimate the PD. Current missing data are critical with regard to low default portfolios, characterised by inadequate default records, making it difficult to design statistically significant prediction models. Several methods might be used to deal with missing data such as list-wise deletion, application-specific list-wise deletion, substitution techniques or imputation models (simple and multiple variants). List-wise deletion is an easy-to-use method widely applied by social scientists, but it loses substantial data and reduces the diversity of information resulting in a bias in the model's parameters, results and inferences. The choice of the best method to solve the missing data problem largely depends on the nature of missing values (MCAR, MAR and MNAR processes) but there is a lack of empirical analysis about their effect on credit risk that limits the validity of resulting models. In this paper, we analyse the nature and effects of missing data in credit risk modelling (MCAR, MAR and NMAR processes) and take into account current scarce data set on consumer borrowers, which include different percents and distributions of missing data. The findings are used to analyse the performance of several methods for dealing with missing data such as likewise deletion, simple imputation methods, MLE models and advanced multiple imputation (MI) alternatives based on MarkovChain-MonteCarlo and re-sampling methods. Results are evaluated and discussed between models in terms of robustness, accuracy and complexity. In particular, MI models are found to provide very valuable solutions with regard to credit risk missing data.     

基于平滑扩充原理的商业银行信用风险评级模型及实证

本文通过银行的资产质量方面、资本充足率方面、管控效能层面、盈利状态层面、流动性层面与社会敏感度层面等构建商业银行信用风险评价体系。根据平滑扩充原理模拟生成大样本数据,对评级得分进行扩充,进而根据扩充后的大样本数据划分银行的信用风险等级。解决了由于样本少、无法对信用等级合理划分的难题。通过实证分析可以了解到,本文得出的银行评级信息和标准普尔提供的评价结论存在共同的序关系状态。因此,可根据本模型对大多数未经过国际权威机构评级的银行进行风险评级。     

基于数据的银行信贷行业的信用风险研究

目前多数研究利用美国旧金山市KMV公司于1997年建立的模型(KMV模型)计算企业年违约距离来评估具体企业的信用风险,但缺乏信贷行业的信用风险评估方法,也不能给出随时间变化的信用风险.首先提出基于数据的信贷行业随时间动态演化的信用风险评估模型,然后利用2016年18个行业的数据得到了中国信贷行业动态演化的信用风险,该信用风险随时间演化特征可分为波动上升、下降后波动、下降后稳定、稳定四种类型.进一步研究发现金融业、科学研究和技术服务业、信息传输软件和技术服务业这三个行业动态演化的信用风险平均值高且不稳定,住宿和餐饮业的信用风险很高但是比较平稳,其他行业的信用风险较低且较平稳.     

Valuing risky debt: A new model combining structural information with the reduced-form approach

A new model of credit risk is proposed in which the intensity of default is described by an additional stochastic differential equation coupled with the process of the obligor’s asset value. Such an approach allows us to incorporate structural information as well as to capture the effect of external factors (e.g. macroeconomic factors) in a both parsimonious and economically consistent way. From the practical standpoint, the proposed model offers great flexibility and allows us to obtain credit spread curves of many different shapes, including double humped term structures. Furthermore, an approximate closed-form solution is derived, which is accurate, easy to implement, and allows for an efficient calibration to realized credit spreads. Numerical experiments are presented showing that the novel approach provides a very satisfactory fitting to market data and outperforms the model developed by Madan and Unal (2000).     

Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

Credit applicants are assigned to good or bad risk classes according to their record of defaulting. Each applicant is described by a high-dimensional input vector of situational characteristics and by an associated class label. A statistical model, which maps the inputs to the labels, can decide whether a new credit applicant should be accepted or rejected, by predicting the class label given the new inputs. Support vector machines (SVM) from statistical learning theory can build such models from the data, requiring extremely weak prior assumptions about the model structure. Furthermore, SVM divide a set of labelled credit applicants into subsets of ‘typical’ and ‘critical’ patterns. The correct class label of a typical pattern is usually very easy to predict, even with linear classification methods. Such patterns do not contain much information about the classification boundary. The critical patterns (the support vectors) contain the less trivial training examples. For instance, linear discriminant analysis with prior training subset selection via SVM also leads to improved generalization. Using non-linear SVM, more ‘surprising’ critical regions may be detected, but owing to the relative sparseness of the data, this potential seems to be limited in credit scoring practice.     

贪污量化的内生随机增长模型

从现实来看,贪污不是一种个人行为,是由于制度上的漏洞(如监管不严等)而产生的.本文假设贪污与廉政均衡模型服从通常的决策规则,试从量化的指标入手,分析了增大个体收入风险,加大惩贪震摄力、扩大公共开支的均衡关系及其对渎职、贪污行为的影响力.最后,给出关于抑制贪污依其影响力大小的措施的不同结论.     

Transmission of macroeconomic shocks to risk parameters: Their uses in stress testing

In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.     

市场利率环境下银企信贷信息共享信号传递博弈模型

在市场利率环境下,运用信号传递博弈理论,在成本倒挂情况下,采取利息补贴承诺的方法,设计一种以风险分担实现利润共享的契约机制,利润共享参数起到了传递信息的信号作用。研究结果表明,在该契约下企业没有撒谎的动机,契约参数是传达信息的信号。该契约机制下不仅可以实现信贷资金供求信息的共享,而且能够保证系统的协调,系统收益达到最优,使企业能够得到新产品生产所需资金,银行解决信贷资金的风险损失的问题。