The Annals of Applied Statistics

Assessment of mortgage default risk via Bayesian state space models

Tevfik Aktekin, Refik Soyer, and Feng Xu

Full-text: Open access

Abstract

Managing risk at the aggregate level is crucial for banks and financial institutions as required by the Basel III framework. In this paper, we introduce discrete time Bayesian state space models with Poisson measurements to model aggregate mortgage default rate. We discuss parameter updating, filtering, smoothing, forecasting and estimation using Markov chain Monte Carlo methods. In addition, we investigate the dynamic behavior of the default rate and the effects of macroeconomic variables. We illustrate the use of the proposed models using actual U.S. residential mortgage data and discuss insights gained from Bayesian analysis.

Article information

Source
Ann. Appl. Stat., Volume 7, Number 3 (2013), 1450-1473.

Dates
First available in Project Euclid: 3 October 2013

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1380804802

Digital Object Identifier
doi:10.1214/13-AOAS632

Mathematical Reviews number (MathSciNet)
MR3127954

Zentralblatt MATH identifier
1283.62211

Keywords
Mortgage default mortgage risk Bayesian inference state space dynamic Poisson process

Citation

Aktekin, Tevfik; Soyer, Refik; Xu, Feng. Assessment of mortgage default risk via Bayesian state space models. Ann. Appl. Stat. 7 (2013), no. 3, 1450--1473. doi:10.1214/13-AOAS632. https://projecteuclid.org/euclid.aoas/1380804802


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