Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD

Zhang, Jie; Thomas, Lyn C.

doi:10.1016/j.ijforecast.2010.06.002

“…For a full overview on AFT models and more technical details we refer to Collett (2003) and Kleinbaum and Klein (2011). AFT models are used in the credit risk context by Narain (1992) (who used an exponential distribution), Banasik et al (1999) (who used exponential and Weibull distributions) and Zhang and Thomas (2012) Using the relationship r ¼ 1 p , it can be shown that a Weibull-distributed random event time T i ¼ expðb 0 x i þ r i Þ corresponds to a survival function…”

Section: Survival Analysis Methodsmentioning

confidence: 99%

Time to default in credit scoring using survival analysis: a benchmark study

Dirick

¹

,

Claeskens

²

,

Baesens

³

2017

Journal of the Operational Research Society

View full text Add to dashboard Cite

We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.

show abstract

“…For a full overview on AFT models and more technical details we refer to Collett (2003) and Kleinbaum and Klein (2011). AFT models are used in the credit risk context by Narain (1992) (who used an exponential distribution), Banasik et al (1999) (who used exponential and Weibull distributions) and Zhang and Thomas (2012) (who used Weibull, log-logistic and gamma distributions). Using the relationship r ¼ 1 p , it can be shown that a Weibull-distributed random event time…”

Section: Survival Analysis Methodsmentioning

confidence: 99%

“…With a high amount of censoring, mean values of these survival analyses do not give good predictors. Zhang and Thomas (2012) compute a predictor for the recovery rate in survival analysis by looking at each percentile of the training set and calculate the squared and absolute deviations from the predictions to the observed values of the default cases. Next, the percentiles resulting in the lowest deviations are withheld and used to compute the deviations in the test set.…”

Section: Evaluation Through Default Time Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Time to Default in Credit Scoring Using Survival Analysis: A Benchmark Study

Dirick

¹

,

Claeskens

²

,

Baesens

³

2015

View full text Add to dashboard Cite

We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.

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

Abdou

¹

,

Tsafack

²

,

Ntim

³

et al. 2016

SSRN Journal

0

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

Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD

Cited by 87 publications

References 12 publications

Time to default in credit scoring using survival analysis: a benchmark study

Time to default in credit scoring using survival analysis: a benchmark study

Time to Default in Credit Scoring Using Survival Analysis: A Benchmark Study

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

Contact Info

Product

Resources

About