Yang, Bill Huajian
(2019):
*Monotonic Estimation for the Survival Probability over a Risk-Rated Portfolio by Discrete-Time Hazard Rate Models.*
Forthcoming in: International Journal of Machine Learning and Computing

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## Abstract

Monotonic estimation for the survival probability of a loan in a risk-rated portfolio is based on the observation arising, for example, from loan pricing that a loan with a lower credit risk rating is more likely to survive than a loan with a higher credit risk rating, given the same additional risk covariates. Two probit-type discrete-time hazard rate models that generate monotonic survival probabilities are proposed in this paper. The first model calculates the discrete-time hazard rate conditional on systematic risk factors. As for the Cox proportion hazard rate model, the model formulates the discrete-time hazard rate by including a baseline component. This baseline component can be estimated outside the model in the absence of model covariates using the long-run average discrete-time hazard rate. This results in a significant reduction in the number of parameters to be otherwise estimated inside the model. The second model is a general form model where loan level factors can be included. Parameter estimation algorithms are also proposed. The models and algorithms proposed in this paper can be used for loan pricing, stress testing, expected credit loss estimation, and modeling of the probability of default term structure.

Item Type: | MPRA Paper |
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Original Title: | Monotonic Estimation for the Survival Probability over a Risk-Rated Portfolio by Discrete-Time Hazard Rate Models |

Language: | English |

Keywords: | loan pricing, survival probability, Cox proportion hazard rate model, baseline hazard rate, forward probability of default, probability of default term structure |

Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C40 - General C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling |

Item ID: | 93398 |

Depositing User: | Dr. Bill Huajian Yang |

Date Deposited: | 24 Apr 2019 14:59 |

Last Modified: | 24 Apr 2019 14:59 |

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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/93398 |