An Estimation Method for Default Probability Based on Sample Matching and An Application

Estimating the default probability is an elementary work in the risk management of commercial banks.Many quantitative models are applied in the area.But size of "bad" samples is much smaller than the size of "good" samples.Biases arise if any quantitative model applied directly on the whole sample,which definitely leads to underestimation of default risk.This paper proposes a method which searches for a good match between "good" and bad samples,then employs logistic model.The method is applied to a provincial bank for its credit asset in the manufacturing industry.The empirical results show that the method produces a balance prediction with a high accuracy,and a good discriminating capacity.