Francisco Louzada — Page 3

arXiv2014

Recovery Risk: Application of the Latent Competing Risks Model to Non performing Loans

This article proposes a method for measuring the latent risks involved in the recovery process of non performing loans in financial institutions and business firms that deal with collection and recovery processes. To that end, we apply the competing risks model referred to in the literature as the promotion time model. The result achieved is the probability of credit recovery for a portfolio segmented into groups based on the information available. Within the context of competing risks, application of the technique yielded an estimation of the number of latent events that concur to the credit recovery event. With these results in hand, we were able to compare groups of defaulters in terms of risk or susceptibility to the recovery event during the collection process, and thereby determine where collection actions are most efficient. We specify the Poisson distribution for the number of latent causes leading to recovery, and the Weibull distribution for the time up to recovery. To estimate the model parameters, we use the maximum likelihood method. Finally, the model was applied to a sample of defaulted loans from a financial institution.

stat.AP↗

arXiv2014

A modified version of the inference function for margins and interval estimation for the bivariate Clayton copula SUR Tobit model: An simulation approach

This paper extends the analysis of bivariate seemingly unrelated regression (SUR) Tobit model by modeling its nonlinear dependence structure through the Clayton copula. The ability in capturing/modeling the lower tail dependence of the SUR Tobit model where some data are censored (generally, at zero point) is an additionally useful feature of the Clayton copula. We propose a modified version of the inference function for margins (IFM) method (Joe and Xu, 1996), which we refer to as MIFM method, to obtain the estimates of the marginal parameters and a better (satisfactory) estimate of the copula association parameter. More specifically, we employ the data augmentation technique in the second stage of the IFM method to generate the censored observations (i.e. to obtain continuous marginal distributions, which ensures the uniqueness of the copula) and then estimate the dependence parameter. Resampling procedures (bootstrap methods) are also proposed for obtaining confidence intervals for the model parameters. A simulation study is performed in order to verify the behavior of the MIFM estimates (we focus on the copula parameter estimation) and the coverage probability of different confidence intervals in datasets with different percentages of censoring and degrees of dependence. The satisfactory results from the simulation (under certain conditions) and empirical study indicate the good performance of our proposed model and methods where they are applied to model the U.S. ready-to-eat breakfast cereals and fluid milk consumption data.

stat.ME↗