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September 2013 Assessment of mortgage default risk via Bayesian state space models
Tevfik Aktekin, Refik Soyer, Feng Xu
Ann. Appl. Stat. 7(3): 1450-1473 (September 2013). DOI: 10.1214/13-AOAS632

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.

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Tevfik Aktekin. Refik Soyer. Feng Xu. "Assessment of mortgage default risk via Bayesian state space models." Ann. Appl. Stat. 7 (3) 1450 - 1473, September 2013. https://doi.org/10.1214/13-AOAS632

Information

Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 1283.62211
MathSciNet: MR3127954
Digital Object Identifier: 10.1214/13-AOAS632

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

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 3 • September 2013
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