Journal of Financial Services Research

, Volume 50, Issue 3, pp 311–339 | Cite as

A Two-Stage Probit Model for Predicting Recovery Rates

  • Ruey-Ching Hwang
  • Huimin Chung
  • C. K. Chu


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.


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

JEL classification

G21 G28 



The authors thank the reviewers for their valuable comments and suggestions that have greatly improved the presentation of this paper. This research is supported by the Ministry of Science and Technology, Taiwan, Republic of China.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of FinanceNational Dong Hwa UniversityHualienTaiwan
  2. 2.Graduate Institute of FinanceNational Chiao Tung UniversityHsinchuTaiwan
  3. 3.Department of Applied MathematicsNational Dong Hwa UniversityHualienTaiwan

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