Sage Journals: Discover world-class research

Abstract

Fluctuation in mortgage default rates provides vital information to financial institutions and is a key indicator of the state of the economy. Using a decade’s worth (2002–2010) of data on prime and subprime mortgage portfolios, we propose and compare two models for mortgage defaults. The first, the Weibull-Gamma segmentation model (WGS), was utilized by Fader and Hardie (2007) in forecasting customer retention. Though effective in that setting, Markov chain Monte Carlo simulations suggest that the WGS suffers from over-parameterization. The Weibull segmentation model (WS) provides a simplified alternative that accurately forecasts default rates while identifying latent classes of “risky” prime and subprime mortgages characterized by increased hazard rates.

Keywords

Mortgage default time-to-default mixture model latent class Bayesian estimation

Get full access to this article

View all access options for this article.

References

Aktekin

Soyer

, & Xu

(2013). Assessment of mortgage default risk via bayesian state space models. Annals of Applied Statistics, 7(3), 1450-1473.

Chen

W. C.

Hill

B. M.

Greenhouse

J. B.

, & Fayes

J. V.

(1985). Bayesian analysis of survival curves for cancer patients following treatment. Bayesian Statistics, 2, 299-328.

Coro

(2017). Gibbs Sampling with JAGS: Behind the Scenes.

Das

, & Stein

R. M.

(2009). Underwriting versus economy: a new approach to decomposing mortgage losses. Journal of Credit Risk, 15(2), 19-41.

Demyanyk

, & Van Hemert

(2011). Understanding the subprime mortgage crisis. Review of Financial Studies, 24(6), 1848-1880.

Erisoglu

, & Erol

(2011). A mixture model of two different distributions approach to the analysis of heterogeneous survival data. International Journal of Computational and Mathematical Sciences, 5(2), 75-79.

Fader

P. S.

, & Hardie

B. G. S.

(2007). How to project customer retention. Journal of Interactive Marketing, 21, 76-90.

Fader

P. S.

Hardie

B. G. S.

, & Zeithammer

(2003). Forecasting new product trial in a controlled test market environment. Journal of Forecasting, 22(5), 391-410.

Glennon

, & Nigro

(2011). Evaluating the performance of static versus dynamic models of credit default: Evidence from long-term Small Business Administration-guaranteed loans. Journal of Credit Risk, 7(2), 3-35.

10.

Hardie

B. G. S.

Fader

P. S.

, & Wisniewski

(1998). An empirical comparison of new product trial forecasting models. Journal of Forecasting, 17, 209-229.

11.

Kiefer

N. M.

(2006). The probability approach to default probabilities. Technical Report, Cornell University.

12.

Kiefer

N. M.

(2008). Default estimation, correlated defaults, and expert information. Technical Report. CAE Working paper #08-02, ISSN 1936-5098.

13.

Marin

J. M.

Rodriguez-Bernal

M. T.

, & Wiper

M. P.

(2005). Using Weibull mixture distributions to model heterogeneous survival data. Communications in Statistics: Simulation and Computation, 34, 673-684.

14.

Plummer

(2016). rjags: Bayesian Graphical Models using MCMC. R package version 4-6.

15.

Popova

, & George

E. I.

(2008). Assessment of mortgage default risk via bayesian reliability models. Bayesian Analysis, 3, 393-426.

16.

Soyer

, & Xu

(2010). Assessment of mortgage default risk via bayesian reliability models. Applied Stochastic Models Bus Ind, 26, 308-330.

Time-to-default analysis of mortgage portfolios

Abstract

Keywords

Get full access to this article

References