|標題:||A Two-Stage Probit Model for Predicting Recovery Rates|
Chu, C. K.
Department of Information Management and Finance
|關鍵字:||Expanding rolling window approach;Ordered probit model;Probit transformation regression;Two-stage probit model;Recovery rate|
|摘要:||We propose a two-stage probit model (TPM) to predict recovery rates. By the ordinal nature of the three categories of recovery rates: total loss, total recovery, and lying between the two extremes, we first use the ordered probit model to predict the category that a given debt belongs to among the three ones. Then, for the debt that is classified as lying between the two extremes, we use the probit transformation regression to predict its recovery rate. We use real data sets to support TPM. Our empirical results show that macroeconomic-, debt-, firm-, and industry-specific variables are all important in determining recovery rates. Using an expanding rolling window approach, our empirical results confirm that TPM has better and more robust out-of-sample performance than its alternatives, in the sense of yielding more accurate predicted recovery rates.|
|期刊:||JOURNAL OF FINANCIAL SERVICES RESEARCH|
|Appears in Collections:||Articles|