Hybrid Credit Research Articles

Firm-specific credit risk estimation in the presence of regimes and noisy prices

Security prices are important inputs for estimating credit risk. Yet, to obtain an accurate firm-specific credit risk assessment, one needs a reliable model and a methodology that filters the elements unrelated to the firm’s fundamentals from market prices.In this article, we introduce a hybrid credit risk model defined in a Markov-switching environment. It captures firm-specific changes in the leverage uncertainty during crises. We also propose a new efficient method to estimate the model, and a numerical scheme based on trinomial lattices to price credit derivatives. The estimation is finally performed on more than 200 firms using maximum likelihood estimation.

Firm-Specific Credit Risk Estimation in the Presence of Regimes and Noisy Prices

Security prices are important inputs for estimating credit risk models. Yet, to obtain an accurate firm-specific credit risk assessment, one needs a reliable model and a methodology that filters all elements unrelated to the firm's fundamentals from observed market prices.Therefore, we first introduce a flexible hybrid credit risk model defined in a Markov-switching environment. It captures firm-specific changes in the leverage uncertainty during crises as well as the negative relationship between creditworthiness and recovery rates. Second, estimation is performed using maximum likelihood by accounting for latent regimes and unobserved noise included in security prices.Using CDS premiums for 225 firms of both CDX North American IG and HY indices, we perform two different empirical applications. The effects of stochastic recovery and the presence of regimes on theoretical credit spread curves are investigated. We also apply the model to corporate bond credit spreads to assess the importance of bond-specific liquidity in the bond-CDS basis.

Credit scoring using ensemble of various classifiers on reduced feature set

Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

Recovery rate risk and credit spreads in a hybrid credit risk model

Recovery rate risk and credit spreads in a hybrid credit risk model

Contagion effect on bond portfolio risk measures in a hybrid credit risk model

This paper illustrates how modelling the contagion effect among assets of a given bond portfolio changes the risk perception associated to it. This empirical work is developed in a hybrid credit risk framework that incorporates recovery rate risk. Dependence structures among firms and between external shocks affecting firms together are considered. The presence of correlations among firm leverage ratios and the interrelation between default probabilities and recovery rates produces clusters of defaults with low recovery rates. This has a major impact on standard risk measures such as Value-at-Risk and conditional tail expectation. Consequently, an appropriate measurement of the contagion has a tremendous effect on the capital requirement of many financial institutions.

Credit Spreads, Recovery Rates and Bond Portfolio Risk Measures in a Hybrid Credit Risk Model

This paper presents a framework in which many structural credit risk models can be made hybrid by randomizing the default trigger, while keeping the capital structure intact. This produces random recovery rates negatively correlated with the default probability. The approach is implemented on a firm-by-firm basis using maximum likelihood and the unscented Kalman filter (UKF) on each of the 225 companies of the CDX NA IG and HY indices using weekly CDS data from December 2007 to January 2012. Adding the surprise element and the time-varying distribution of recovery rates has a large impact on credit spreads as it modifies both the level and shape of the curves. When a bond portfolio is considered, the presence of dependence among firm leverage ratios and between default probabilities and recovery rates produces clusters of defaults with low recovery rates. It has a major impact on standard risk measures such as Value- at-Risk and conditional tail expectation.

Accessible online learning

AbstractWith the majority of public postsecondary institutions offering online or hybrid credit courses and 11 percent of postsecondary students having a documented disability, the accessibility of online courses cannot be ignored.

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

Combination of feature selection approaches with SVM in credit scoring

The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will objectively help managers instead of intuitive experience. This study proposes four approaches combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different credit scoring models are constructed by selecting attributes with four approaches. Two UCI (University of California, Irvine) data sets are chosen to evaluate the accuracy of various hybrid-SVM models. SVM classifier combines with conventional statistical LDA, Decision tree, Rough sets and F-score approaches as features pre-processing step to optimize feature space by removing both irrelevant and redundant features. In this paper, the procedure of the proposed approaches will be described and then evaluated by their performances. The results are compared in combination with SVM classifier and nonparametric Wilcoxon signed rank test will be held to show if there is any significant difference between these models. The result in this study suggests that hybrid credit scoring approach is mostly robust and effective in finding optimal subsets and is a promising method to the fields of data mining.

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks.