Time Matters: How Default Resolution Times Impact Final Loss Rates

Betz, Jennifer; Kellner, Ralf; Rösch, Daniel

doi:10.1111/rssc.12474

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…For both frailty models, the frailty parameters (σ 2 and ζ, respectively) are significant at the 5% level based on the adjusted log-likelihood test. In line with the findings of Khieu et al [42] and Betz et al [43], a negative relationship between GDP growth and time to write-off is observed. This means that higher GDP growth at the time of default is associated with a higher probability of full loan repayment.…”

Section: Real-world Datasetsupporting

confidence: 89%

“…Our choice of GDP growth is motivated by the study of Khieu et al [42], in which the authors report a positive relationship between annual GDP growth and recovery rates of defaulted corporate debt in the US. Furthermore, Betz et al [43] found positive dependencies between default resolution times and final loan loss rates. The MLEs of the model parameters are shown in the first three columns of Table 5.…”

Section: Real-world Datasetmentioning

confidence: 98%

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Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

et al. 2023

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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.

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Section: Real-world Datasetsupporting

confidence: 89%

Section: Real-world Datasetmentioning

confidence: 98%

Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

et al. 2023

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“…Second, with respect to the downturn estimates, the random effects structure gives financial institutions as well as prudential regulators a great flexibility to apply their margin of conservatism individually. 11 Alternatively, we could have used finite mixture models as in Calabrese (2014), Altman and Kalotay (2014), Kalotay and Altman (2017), Betz et al (2018) or Betz et al (2021) for Losses Given Default (LGDs). These models assume a latent variable which describes the affiliation to individual components of the mixture model and use observable and unobservable covariates to model this latent variable.…”

Section: Methodsmentioning

confidence: 99%

Credit Line Exposure at Default Modelling Using Bayesian Mixed Effect Quantile Regression

Betz

Nagl

Rösch

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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For banks, credit lines play an important role exposing both liquidity and credit risk. In the advanced internal ratings-based approach, banks are obliged to use their own estimates of exposure at default using credit conversion factors. For volatile segments, additional downturn estimates are required. Using the world's largest database of defaulted credit lines from the US and Europe and macroeconomic variables, we apply a Bayesian mixed effect quantile regression and find strongly varying covariate effects over the whole conditional distribution of credit conversion factors and especially between United States and Europe. If macroeconomic variables do not provide adequate downturn estimates, the model is enhanced by random effects. Results from European credit lines suggest that high conversion factors are driven by random effects rather than observable covariates. We further show that the impact of the economic surrounding highly depends on the level of utilization one year prior default, suggesting that credit lines with high drawdown potential are most affected by economic downturns and hence bear the highest risk in crisis periods.

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“…They introduce a special type of combinatory stochastic process based on a complex system of assumptions, referring to a discretization of recovery rates in m levels. Betz, Kellner, and Rosch [28] develop a joint modeling framework for default resolution time (duration of loans) and LGD, also considering the censoring effects of unresolved loan contracts. They develop a hierarchical Bayesian model for joint estimation of default resolution time and LGD, including survival modeling techniques applicable to duration processes.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Recovery Performance of a Portfolio of NPLs

2023

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The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the recovery rates over time, here assumed discretized, for example, in years. In this way, the user can get simultaneously information about recovery rate and time to liquidate of the portfolio. In particular, it is discussed how to estimate such a curve in the presence of right-censored data, e.g., when the NPLs composing the portfolio have been observed in different time periods, with a method based on an algorithm that is usually used in the construction of survival curves. The curves obtained are smoothed with nonparametric statistical learning techniques. The effectiveness of the proposal is shown by applying the method to simulated and real financial data. The latter are about some portfolios of Italian unsecured NPLs taken over by a specialized operator.

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Time Matters: How Default Resolution Times Impact Final Loss Rates

Cited by 8 publications

References 42 publications

Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

Credit Line Exposure at Default Modelling Using Bayesian Mixed Effect Quantile Regression

Measuring the Recovery Performance of a Portfolio of NPLs

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