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Mathematics
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Application of Survival Analysis in Economics, Finance and Insurance
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Application of Survival Analysis in Economics, Finance and Insurance

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 10 January 2025 | Viewed by 9308

Special Issue Editor

Dr. David Zapletal

E-Mail Website
Guest Editor
Science and Research Centre, Faculty of Economics and Administration, University of Pardubice, 532 10 Pardubice, Czech Republic
Interests: survival analysis; regression modelling; time series analysis; data analysis

Special Issue Information

Dear Colleagues,

It is my pleasure to announce a Special Issue on “Application of Survival Analysis in Economics, Finance and Insurance”. Survival analysis deals with time-to-event data and has been well established in the medical science and engineering field. In recent decades, survival analysis has also found application in the fields of economics, finance and insurance. This Special Issue will focus on recent advances in survival analysis applications and their specific problems in these areas. Special Issue topics include (but are not limited to) various regression models, discrete time models, mixture cure models, competing risks models, inclusion of time-dependent variables, multiple events and recurrent events modeling, etc. Applications of machine learning methods in survival analysis, such as random survival forests, boosting or deep neural networks, are also welcome.

I look forward to receiving your interesting submissions.

Dr. David Zapletal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • survival analysis
  • censoring
  • regression models
  • mixture cure models
  • competing risks

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Published Papers (4 papers)

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Research

23 pages, 1397 KiB  
Article
An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling
by Joseph L. Breeden
Mathematics 2024, 12(10), 1427; https://doi.org/10.3390/math12101427 - 7 May 2024
Viewed by 575
Abstract
The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. [...] Read more.
The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets. Full article
(This article belongs to the Special Issue Application of Survival Analysis in Economics, Finance and Insurance)
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17 pages, 663 KiB  
Article
Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty
by Janette Larney, James Samuel Allison, Gerrit Lodewicus Grobler and Marius Smuts
Mathematics 2023, 11(10), 2228; https://doi.org/10.3390/math11102228 - 9 May 2023
Viewed by 1790
Abstract
Modelling the outcome after loan default is receiving increasing attention, and survival analysis is particularly suitable for this purpose due to the likely presence of censoring in the data. In this study, we suggest that the time to loan write-off may be influenced [...] Read more.
Modelling the outcome after loan default is receiving increasing attention, and survival analysis is particularly suitable for this purpose due to the likely presence of censoring in the data. In this study, we suggest that the time to loan write-off may be influenced by latent competing risks, as well as by common, unobservable drivers, such as the state of the economy. We therefore expand on the promotion time cure model and include a parametric frailty parameter to account for common, unobservable factors and for possible observable covariates not included in the model. We opt for a parametric model due to its interpretability and analytical tractability, which are desirable properties in bank risk management. Both a gamma and inverse Gaussian frailty parameter are considered for the univariate case, and we also consider a shared frailty model. A Monte Carlo study demonstrates that the parameter estimation of the models is reliable, after which they are fitted to a real-world dataset in respect of large corporate loans in the US. The results show that a more flexible hazard function is possible by including a frailty parameter. Furthermore, the shared frailty model shows potential to capture dependence in write-off times within industry groups. Full article
(This article belongs to the Special Issue Application of Survival Analysis in Economics, Finance and Insurance)
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16 pages, 1439 KiB  
Article
A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data
by Jorge Luis Andrade and José Luis Valencia
Mathematics 2023, 11(1), 198; https://doi.org/10.3390/math11010198 - 30 Dec 2022
Cited by 4 | Viewed by 2745
Abstract
We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning [...] Read more.
We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning technique for solving time-to-event data using an ensemble of multiple fuzzy survival trees. In the learning process, the combination of methods such as the c-index, fuzzy sets theory, and the ensemble of multiple trees enable the automatic handling of imprecise data. We analyse the results of several experiments and test them statistically; they show the FRSF’s robustness, verifying that its generalisation capacity is not reduced when modelling imprecise data. Furthermore, the results obtained using a real portfolio of a life insurance company demonstrate that the FRSF has a better performance in comparison with other state-of-the-art algorithms such as the traditional Cox model and other tree-based machine learning techniques such as the random survival forest. Full article
(This article belongs to the Special Issue Application of Survival Analysis in Economics, Finance and Insurance)
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25 pages, 691 KiB  
Article
Bootstrap Bandwidth Selection and Confidence Regions for Double Smoothed Default Probability Estimation
by Rebeca Peláez, Ricardo Cao and Juan M. Vilar
Mathematics 2022, 10(9), 1523; https://doi.org/10.3390/math10091523 - 2 May 2022
Cited by 3 | Viewed by 1820
Abstract
For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b. [...] Read more.
For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b. This probability is one of the most crucial elements that influences the risk in credits. Previous works have proposed nonparametric estimators for the probability of default derived from Beran’s estimator and a doubly smoothed Beran’s estimator of the conditional survival function for censored data. They have also found asymptotic expressions for the bias and variance of the estimators, but they do not provide any practical way to choose the smoothing parameters involved. In this paper, resampling methods based on bootstrap techniques are proposed to approximate the bandwidths on which Beran and smoothed Beran’s estimators of the PD depend. Bootstrap algorithms for the calculation of confidence regions of the probability of default are also proposed. Extensive simulation studies show the good behavior of the presented algorithms. The bandwidth selector and the confidence region algorithm are applied to a German credit dataset to analyze the probability of default conditional on the credit scoring. Full article
(This article belongs to the Special Issue Application of Survival Analysis in Economics, Finance and Insurance)
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