Reduced credit risk measurement model with particle filter approach

At present, tremendous evidences indicate reduced credit risk measurement models are comparatively accurate. However, this kind of model is complex. Thereby, it is often difficult to estimate parameters of it. To solve this problem, this paper uses adaptive estimation algorithm based on the combination of particle filter and simultaneous perturbation stochastic approximation to estimate parameters of the reduced credit risk measurement model. Moreover, this paper compares particle filter approach with Markov-chain Monte Carlo (MCMC). The estimation accuracy of the two approaches is basically identical. However, the calculation speed of particle filter is quicker than MCMC. Finally, authors do empirical analysis with American personal housing mortgage loan data.