What is LOGIT models?
There are a number of models uses for default prediction and Logistic regression models (also known as LOGIT models) is one of the popular ones. LOGIT model is rom the Generalized Linear Model (GLM) family. It is primarily widely used as a statistical tool to predict default. These types of
models are based on analyzing the dependencies of one or multiple dependent variables from one or more independent variables.
Example of LOGIT models:
Let’s assume that π represents the probability that a default event takes place. The link function represents the logarithm of the ratio between the default probability and the probability that the firm continues to be a performing borrower (the ratio is known as odds). The LOGIT (i.e., the logarithm of
odds) equation is therefore:
LOGIT (π i) = log π i/ 1- π i
The LOGIT function associates the expected value for the dependent variable to the linear combination of independent variables, whereas the relationship between the probability of default (π) and the independent variables is nonlinear.
Why is the LOGIT model important?
LOGIT model provides a strong model to assist risk managers in predicting the default of the bank’s portfolio and is widely used as the based model for predictive modelling.