Comparison of Survival Analysis Approaches to Modelling Credit Risks

Sammy Mungasi; Collins Odhiambo

doi:doi:10.11648/j.ajtas.20190802.11

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Comparison of Survival Analysis Approaches to Modelling Credit Risks

Sammy Mungasi, Collins Odhiambo

Published in American Journal of Theoretical and Applied Statistics (Volume 8, Issue 2)

Received: 1 April 2019 Accepted: 15 May 2019 Published: 5 June 2019

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Abstract

Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.

Published in	American Journal of Theoretical and Applied Statistics (Volume 8, Issue 2)
DOI	10.11648/j.ajtas.20190802.11
Page(s)	39-46
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Credit Risk, Default Rates, Cox Proportional Hazard Model, Mixture Cure Model, Hazard Ratio

References

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APA Style

Sammy Mungasi, Collins Odhiambo. (2019). Comparison of Survival Analysis Approaches to Modelling Credit Risks. American Journal of Theoretical and Applied Statistics, 8(2), 39-46. https://doi.org/10.11648/j.ajtas.20190802.11

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ACS Style

Sammy Mungasi; Collins Odhiambo. Comparison of Survival Analysis Approaches to Modelling Credit Risks. Am. J. Theor. Appl. Stat. 2019, 8(2), 39-46. doi: 10.11648/j.ajtas.20190802.11

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AMA Style

Sammy Mungasi, Collins Odhiambo. Comparison of Survival Analysis Approaches to Modelling Credit Risks. Am J Theor Appl Stat. 2019;8(2):39-46. doi: 10.11648/j.ajtas.20190802.11

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@article{10.11648/j.ajtas.20190802.11,
  author = {Sammy Mungasi and Collins Odhiambo},
  title = {Comparison of Survival Analysis Approaches to Modelling Credit Risks},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {8},
  number = {2},
  pages = {39-46},
  doi = {10.11648/j.ajtas.20190802.11},
  url = {https://doi.org/10.11648/j.ajtas.20190802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20190802.11},
  abstract = {Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.},
 year = {2019}
}

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TY  - JOUR
T1  - Comparison of Survival Analysis Approaches to Modelling Credit Risks
AU  - Sammy Mungasi
AU  - Collins Odhiambo
Y1  - 2019/06/05
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtas.20190802.11
DO  - 10.11648/j.ajtas.20190802.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 39
EP  - 46
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20190802.11
AB  - Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.
VL  - 8
IS  - 2
ER  -

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Author Information

Sammy Mungasi

Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya
Collins Odhiambo

Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

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APA Style

Sammy Mungasi, Collins Odhiambo. (2019). Comparison of Survival Analysis Approaches to Modelling Credit Risks. American Journal of Theoretical and Applied Statistics, 8(2), 39-46. https://doi.org/10.11648/j.ajtas.20190802.11

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ACS Style

Sammy Mungasi; Collins Odhiambo. Comparison of Survival Analysis Approaches to Modelling Credit Risks. Am. J. Theor. Appl. Stat. 2019, 8(2), 39-46. doi: 10.11648/j.ajtas.20190802.11

Copy | Download

AMA Style

Sammy Mungasi, Collins Odhiambo. Comparison of Survival Analysis Approaches to Modelling Credit Risks. Am J Theor Appl Stat. 2019;8(2):39-46. doi: 10.11648/j.ajtas.20190802.11

Copy | Download

@article{10.11648/j.ajtas.20190802.11,
  author = {Sammy Mungasi and Collins Odhiambo},
  title = {Comparison of Survival Analysis Approaches to Modelling Credit Risks},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {8},
  number = {2},
  pages = {39-46},
  doi = {10.11648/j.ajtas.20190802.11},
  url = {https://doi.org/10.11648/j.ajtas.20190802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20190802.11},
  abstract = {Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Comparison of Survival Analysis Approaches to Modelling Credit Risks
AU  - Sammy Mungasi
AU  - Collins Odhiambo
Y1  - 2019/06/05
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtas.20190802.11
DO  - 10.11648/j.ajtas.20190802.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 39
EP  - 46
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20190802.11
AB  - Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.
VL  - 8
IS  - 2
ER  -

Copy | Download