Assessment of loan losses after default

TL;DR

This paper presents a method to estimate post-default loan losses (LGD) considering repayment dynamics, using Bayesian estimation and a recovery model to ensure LGD values are within valid bounds.

Contribution

It introduces a Bayesian scheme and a general recovery model for more accurate LGD estimation after default, accounting for repayment periods and default timing.

Findings

Bayesian scheme effectively estimates repayable debts.

The recovery model ensures LGD ≤ 1.

Method accounts for default timing and repayment volumes.

Abstract

The paper shows how to determine the loss on an LGD borrower's loan after default, with or without preparation of a separate model. LGD after default is estimated taking into account the average repayment period of the defaulted loan, knowledge of volumes, moments of default and repayments, the rate or other parameters in the vector of determinants. The calculation of the average repayment period for overdue loans is given in the article. A Bayesian scheme is used to estimate repayable debts, considering the percentage of repayment. A general recovery model was used for the LGD segment recovery process. Only this type of model allows you to set LGD less than or equal to 1, which is required for further estimates.