A Mortality Approach for Estimating the Probability of Default in Credit Risk

Abstract

Credit risk is an important problem in banking, finance and insurance sectors. The financial institutions have to use information related to some specific characteristics of borrowers, such as borrower reputation, capital and capacity in order to make the right decision whether or not to grant credit. Regulatory capital allows banks to better capture unique risks and exposure cash flows. In this work, we propose a new approach to estimate the probability of default (PD) by using mortality tables based on progressive censored. The PD is a crucial value, which is used in computing the required capital against credit risk exposures. We use the internal rate approach and measure the PD based on time of default. Then we create a new model for Progressive censored data. The new model takes advantage simultaneously from both the progressively censored and Moody’s models, and where the objective is to compute the cumulative probability. The overall proposed approach is illustrated by examples to show its applicability and demonstrate its performance. Our research findings show that the PD based survival analysis for progressive censored increases each year similar to Moody’s. It impacts on worst –case default rate and the maturity adjustment.