Market-Based Credit Ratings

We present a methodology for rating in real-time the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public firms whose assets are traded to be directly rated by market participants. For firms whose assets are not traded, we show how they can be indirectly rated by matching them to firms that are traded based on observable characteristics. We also show how the resulting ratings can be used to construct loss distributions for portfolios of bonds. Finally, we compare our ratings to Standard & Poors and find that, over the period 2005 to 2011, our ratings lead theirs for firms that ultimately default.     

中国信贷市场的配适性分析

为了研究中国信贷市场供求配适性状况,以及造成中国信贷投放总量错配的主要因素,文章利用1997-2009年2季度中国信贷市场季度数据,采用最大似然方法估计信贷供求非均衡模型参数,实证结果表明：（1）信贷供给小于信贷需求为32个季度,信贷供给大于信贷需求为15个季度,其中1997-2001年以及2005-2007年存在严重的供小于求现象;而2002-2004年及2008年3季度以来存在信贷供大于求现象,其中2009年第1季度信贷超额供给占观察到的实际信贷量的比例为18.37%;（2）中国信贷市场上银行信贷能力是影响信贷供给的重要变量,信贷能力越高,社会上的贷款就越多,而2009年来的信贷大幅投放已经超过了银行的实际信贷能力。     

Parameter Estimation in Credit Models Under Incomplete Information

We consider the filtering model of Frey and Schmidt (2012 Frey , R. , Schmidt , T. ( 2012 ). Pricing and hedging of credit derivatives via the innovations approach to nonlinear filtering . Fin. Stocha. 16 ( 1 ): 105 – 133 .[Crossref], [Web of Science ®] , [Google Scholar]) stated under the real probability measure and develop a method for estimating the parameters in this framework by using time-series data of CDS index spreads and classical maximum-likelihood algorithms. The estimation-approach incorporates the Kushner-Stratonovich SDE for the dynamics of the filtering probabilities. The convenient formula for the survival probability is a prerequisite for our estimation algorithm. We apply the developed maximum-likelihood algorithms on market data for historical CDS index spreads (iTraxx Europe Main Series) in order to estimate the parameters in the nonlinear filtering model for an exchangeable credit portfolio. Several such estimations are performed as well as accompanying statistical and numerical computations.     

A Discrete Variable Chain Graph for Applicants for Credit

A bank offering unsecured personal loans may be interested in several related outcome variables, including defaulting on the repayments, early repayment or failing to take up an offered loan. Current predictive models used by banks typically consider such variables individually. However, the fact that they are related to each other, and to many interrelated potential predictor variables, suggests that graphical models may provide an attractive alternative solution. We developed such a model for a data set of 15 variables measured on a set of 14 000 applications for unsecured personal loans. The resulting global model of behaviour enabled us to identify several previously unsuspected relationships of considerable interest to the bank. For example, we discovered important but obscure relationships between taking out insurance, prior delinquency with a credit card and delinquency with the loan.     

WLAD-LASSO method for robust estimation and variable selection in partially linear models

This paper focuses on robust estimation and variable selection for partially linear models. We combine the weighted least absolute deviation (WLAD) regression with the adaptive least absolute shrinkage and selection operator (LASSO) to achieve simultaneous robust estimation and variable selection for partially linear models. Compared with the LAD-LASSO method, the WLAD-LASSO method will resist to the heavy-tailed errors and outliers in the parametric components. In addition, we estimate the unknown smooth function by a robust local linear regression. Under some regular conditions, the theoretical properties of the proposed estimators are established. We further examine finite-sample performance of the proposed procedure by simulation studies and a real data example.     

Bayesian variable selection in a finite mixture of linear mixed-effects models

Mixture of linear mixed-effects models has received considerable attention in longitudinal studies, including medical research, social science and economics. The inferential question of interest is often the identification of critical factors that affect the responses. We consider a Bayesian approach to select the important fixed and random effects in the finite mixture of linear mixed-effects models. To accomplish our goal, latent variables are introduced to facilitate the identification of influential fixed and random components and to classify the membership of observations in the longitudinal data. A spike-and-slab prior for the regression coefficients is adopted to sidestep the potential complications of highly collinear covariates and to handle large p and small n issues in the variable selection problems. Here we employ Markov chain Monte Carlo (MCMC) sampling techniques for posterior inferences and explore the performance of the proposed method in simulation studies, followed by an actual psychiatric data analysis concerning depressive disorder.     

A Note on Item Response Theory Modeling for Online Customer Ratings

ABSTRACT

Online consumer product ratings data are increasing rapidly. While most of the current graphical displays mainly represent the average ratings, Ho and Quinn proposed an easily interpretable graphical display based on an ordinal item response theory (IRT) model, which successfully accounts for systematic interrater differences. Conventionally, the discrimination parameters in IRT models are constrained to be positive, particularly in the modeling of scored data from educational tests. In this article, we use real-world ratings data to demonstrate that such a constraint can have a great impact on the parameter estimation. This impact on estimation was explained through rater behavior. We also discuss correlation among raters and assess the prediction accuracy for both the constrained and the unconstrained models. The results show that the unconstrained model performs better when a larger fraction of rater pairs exhibit negative correlations in ratings.     

A Markov Chain Copula Model for Credit Default Swaps with Bilateral Counterparty Risk

Reduced-form credit risk models are widely used in pricing and hedging credit derivatives. Generating default dependency is the key element in any such model. In this article, we use Markov copulae approach to model the dependence structure of defaults between the three obligors, one is the reference entity, another is the protection seller, the other is the protection buyer(the investor), so we can consider the bilateral counterparty risk of credit default swaps(CDS). In this Markov chain copula model, we obtain the explicit formulas of the CDS premium rates C ₁(T) (with unilateral counterparty risk) and C ₂(T) (with bilateral counterparty risk). And then we perform some numerical experiments to analyze the difference of the fair spreads between the unilateral case and the bilateral case.     

Credit risk assessment with Bayesian model averaging

Many credit risk models are based on the selection of a single logistic regression model, on which to base parameter estimation. When many competing models are available, and without enough guidance from economical theory, model averaging represents an appealing alternative to the selection of single models. Despite model averaging approaches have been present in statistics for many years, only recently they are starting to receive attention in economics and finance applications. This contribution shows how Bayesian model averaging can be applied to credit risk estimation, a research area that has received a great deal of attention recently, especially in the light of the global financial crisis of the last few years and the correlated attempts to regulate international finance. The paper considers the use of logistic regression models under the Bayesian Model Averaging paradigm. We argue that Bayesian model averaging is not only more correct from a theoretical viewpoint, but also slightly superior, in terms of predictive performance, with respect to single selected models.     

关联方增信与资产证券化信用风险溢价——基于底层资产风险的统计分析

本文着眼于企业资产支持证券(ABS)市场的信用风险溢价,探讨关联方增信的效果。研究表明,采用关联方增信的ABS风险溢价较高;当同一主体先前发行的ABS评级上调时,新发行ABS的风险溢价会因为采用关联方增信而升高。本文进一步探究了产生上述现象的原因,一方面,关联方增信使得集团内部风险集聚;另一方面,企业的道德风险使得原ABS的评级上调成为反映新ABS底层资产质量变差的负面信号,从而影响投资者的定价判断。本研究揭示了关联方增信可能存在的道德风险问题。通过分析ABS丰富的底层资产统计特征,本文发现集团风险集聚导致ABS的底层资产质量下降,进而提高了企业的融资成本。本文建议监管机构可以通过适当限制关联方增信或加强监管的方式保障我国企业资产证券化市场的健康持续发展。     

The large-sample performance of backwards variable elimination

Prior studies have shown that automated variable selection results in models with substantially inflated estimates of the model R ², and that a large proportion of selected variables are truly noise variables. These earlier studies used simulated data sets whose sample sizes were at most 100. We used Monte Carlo simulations to examine the large-sample performance of backwards variable elimination. We found that in large samples, backwards variable elimination resulted in estimates of R ² that were at most marginally biased. However, even in large samples, backwards elimination tended to identify the correct regression model in a minority of the simulated data sets.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

A simultaneous variable selection methodology for linear mixed models

Selecting an appropriate structure for a linear mixed model serves as an appealing problem in a number of applications such as in the modelling of longitudinal or clustered data. In this paper, we propose a variable selection procedure for simultaneously selecting and estimating the fixed and random effects. More specifically, a profile log-likelihood function, along with an adaptive penalty, is utilized for sparse selection. The Newton-Raphson optimization algorithm is performed to complete the parameter estimation. By jointly selecting the fixed and random effects, the proposed approach increases selection accuracy compared with two-stage procedures, and the usage of the profile log-likelihood can improve computational efficiency in one-stage procedures. We prove that the proposed procedure enjoys the model selection consistency. A simulation study and a real data application are conducted for demonstrating the effectiveness of the proposed method.     

Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT

Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.     

Variable Selection in Linear Mixed Models Using an Extended Class of Penalties

There is an emerging need to advance linear mixed model technology to include variable selection methods that can simultaneously choose and estimate important effects from a potentially large number of covariates. However, the complex nature of variable selection has made it difficult for it to be incorporated into mixed models. In this paper we extend the well known class of penalties and show that they can be integrated succinctly into a linear mixed model setting. Under mild conditions, the estimator obtained from this mixed model penalised likelihood is shown to be consistent and asymptotically normally distributed. A simulation study reveals that the extended family of penalties achieves varying degrees of estimator shrinkage depending on the value of one of its parameters. The simulation study also shows there is a link between the number of false positives detected and the number of true coefficients when using the same penalty. This new mixed model variable selection (MMVS) technology was applied to a complex wheat quality data set to determine significant quantitative trait loci (QTL).     

Generalized additive models with unknown link function including variable selection

The generalized additive model is a well established and strong tool that allows modelling smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, estimates can be biased. A procedure is proposed that simultaneously estimates the form of the link function and the unknown form of the predictor functions including selection of predictors. The procedure is based on boosting methodology, which obtains estimates by using a sequence of weak learners. It strongly dominates fitting procedures that are unable to modify a given link function if the true link function deviates from the fixed function. The performance of the procedure is shown in simulation studies and illustrated by real world examples.     

经济地位对信用卡逾期的影响

我国信用卡业务的迅猛发展助推了消费经济的快速发展,但信用卡的逾期行为不容忽视。收入代表了一个人的经济地位,是信用卡按时还款的保障。本文基于某商业银行信用卡客户的逾期数据,以持卡人的经济地位为视角,分析了经济地位对信用卡逾期行为的影响。研究结果表明,我国商业银行信用卡持卡人的逾期行为具有显著的经济特征,收入对信用卡逾期的影响呈“U”型的非线性特征,即收入较低和收入较高的持卡人逾期的可能性较高,收入处于中间的持卡人逾期的可能性较低。进一步的研究发现,中年群体、工作单位稳定者、有房者会降低经济地位对信用卡逾期行为的非线性影响,而账龄较长的持卡人提升了这种影响。本文的研究为全社会建立良好的信用卡用卡环境,商业银行高效处理信用卡逾期,改进和完善商业银行信用卡风险管理提供了关键证据。     

融资约束如何影响劳动收入份额

本文基于流动资本的视角,新古典要素分配模型中引入融资约束,分析了企业流动资本约束对劳动收入份额影响机制;进一步建立相应的计量模型,采用1998-2007年中国工业企业数据实证考察了融资约束对劳动收入份额的影响效应。研究表明与流动资本紧密相关的内源融资约束对于劳动收入份额的变化具有显著的负向影响,尤其是对于非国有企业表现更为明显;外源融资约束的加强在一定程度上则可能减少企业流动资本的挤占,从而增加劳动收入份额;企业垄断能力的变化及其实收资本中不同所有制所属份额,都将显著改变融资约束影响要素收入份额的程度。     

信贷约束、风险态度与家庭资产选择

本文运用中国家庭金融调查数据（CHFS）,从信贷约束与风险态度两个方面研究其对家庭资产的参与及配置影响。研究发现,在控制其他因素情况下,家庭信贷约束会增加家庭风险厌恶程度;受到信贷约束的家庭,其房产持有概率和房产市值均显著下降;其股票持有概率会显著下降,但对其持有股票市值影响并不显著;受到信贷约束的家庭,其购买商业保险的概率偏低;家庭风险态度对家庭房产选择的影响不显著;对股票资产的持有概率和持有量均产生负向影响,对商业保险资产的持有则产生显著正向影响。