A Two-Stage Probit Model for Predicting Recovery Rates

Ruey Ching Hwang*, Huimin Chung, C. K. Chu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)311-339
Number of pages29
JournalJournal of Financial Services Research
Issue number3
StatePublished - 1 Dec 2016


  • Expanding rolling window approach
  • Ordered probit model
  • Probit transformation regression
  • Recovery rate
  • Two-stage probit model

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