Credit Risk Measurement Based on the Markov Chain

Abstract

Credit migration matrices are often used in many credit risk and pricing application, and typically assumed to be generated by a simple Markov process. This paper is going to analyze the basic elements of credit risk research, and Maximum Likelihood estimation will be adopted to estimate the Mover-Stayer model’s parameters in this paper. Furthermore, the recursive method will be used to compute the Maximum Likelihood estimator, and the numerical results can illustrate the strength of the Mover-Stayer model on credit risk analysis. We also use the hypotheses to prove that the Markov chain suit for the data against the hypotheses that the Mover-Stayer model more suitable for the data. Finally, we will make some comparisons according to the output of the program, and obtain some conclusions. The Mover-Stayer Model is more suitable against according the numbered result.