Loss given default models incorporating macroeconomic variables for credit cards

Bellotti, Tony; Crook, Jonathan

doi:10.1016/j.ijforecast.2010.08.005

Cited by 122 publications

(84 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, with the financial crisis in 2008, awareness and the importance of credit risk management have increased, and new insights were gained, especially in terms of how correlated loans losses, debtor behaviour and the economic climate can be. In that, there has been work in the corporate sector, estimating Probability of Default (PD) and Loss Given Default (LGD) models with the inclusion of macroeconomic variables (for example, see Frye (2000aFrye ( , 2000b for PD; Stein (2002, 2005) for LGD), but only recently has this been undertaken for retail loan credit models (for example, see Bellotti and Crook (2010), PenningtonCross (2003) for PD; Bellotti and Crook (2012), Leow et al (2011) for LGD).…”

Section: Introductionmentioning

confidence: 99%

Intensity models and transition probabilities for credit card loan delinquencies

Leow

Crook

2014

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

Intensity models and transition probabilities for credit card loan delinquenciesCitation for published version: Leow, M & Crook, J 2014, 'Intensity models and transition probabilities for credit card loan delinquencies' European Journal of Operational Research, vol. 236, pp. 685-694. DOI: 10.1016/j.ejor.2013 General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Intensity models and transition probabilities for credit card loan delinquencies AbstractWe estimate the probability of delinquency and default for a sample of credit card loans using intensity models, via semi-parametric multiplicative hazard models with time-varying covariates. It is the first time these models, previously applied for the estimation of rating transitions, are used on retail loans. Four states are defined in this non-homogenous Markov chain: up-to-date, one month in arrears, two months in arrears, and default; where transitions between states are affected by individual characteristics of the debtor at application and their repayment behaviour since.These intensity estimations allow for insights into the factors that affect movements towards (and recovery from) delinquency, and into default (or not). Results indicate that different types of debtors behave differently while in different states. The probabilities estimated for each type of transition are then used to make out-ofsample predictions over a specified period of time.

show abstract

Section: Introductionmentioning

confidence: 99%

Intensity models and transition probabilities for credit card loan delinquencies

Leow

Crook

2014

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The development of EAD and LGD prediction models has become a popular topic in recent research (e.g., Bellotti & Crook, 2012;Loterman et al, 2012;Somers & Whittaker, 2007).…”

Section: Related Workmentioning

confidence: 99%

Data mining techniques: Modern approaches to application in credit scoring

Volkova¹,

Гисин²,

Solov'ev³

2017

View full text Add to dashboard Cite

state-of-the-art in credit scoring. In addition, we repeat our benchmark for several performance measures and examine the degree to which these differ in their assessment of candidate scorecards. Our study provides valuable insight for professionals and academics in credit scoring.The paper helps practitioners to stay abreast of technical advancements in predictive modeling.From an academic standpoint, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.

show abstract

“…CART can be used to analyse either quantitative or categorical data and is widely used in building scoring models (e.g. [10,13,16,32,39,59,60] ).…”

Section: Related Studiesmentioning

confidence: 99%

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

et al. 2016

View full text Add to dashboard Cite

a b s t r a c tThe preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models' performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

show abstract

Loss given default models incorporating macroeconomic variables for credit cards

Cited by 122 publications

References 11 publications

Intensity models and transition probabilities for credit card loan delinquencies

Intensity models and transition probabilities for credit card loan delinquencies

Data mining techniques: Modern approaches to application in credit scoring

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

Contact Info

Product

Resources

About