A Forecasting Model for the Likelihood of Delinquency, Default or Prepayment : The Case of Taiwan

In a competitive and dynamic market, financial institutions must forecast the proportion of mortgages that will become delinquent, default or prepay. This paper develops a novel forecasting model with nonstationary Markov chain and Grey forecasting, capable of predicting the likelihood of delinquency, default and prepayment. Home mortgage data, obtained by a major Taiwan financial institution from January 1, 1996 to June 30, 1998, are adopted to examine the forecasting effectiveness of the novel forecasting model and the ARIMA model. Empirical results indicate that the novel forecasting model with a low error is better than ARIMA. Thus, the novel forecasting model provides a promising means of accurately predicting the probabilities of delinquency, default and prepayment