Counting Processes for Retail Default Modeling

Nicholas Kiefer () and C. Erik Larson ()
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C. Erik Larson: Promontory Financial Group, LLC, Postal: 801 17th Street, NW, Suite 1100, Washington, DC 20006, USA

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: Counting processes provide a very flexible framework for modeling discrete events occurring over time. Estimation and interpretation is easy, and links to more familiar approaches are at hand. The key is to think of data as "event histories," a record of times of switching between states in a discrete state space. In a simple case, the states could be default/non-default; in other models relevant for credit modeling the states could be credit scores or payment status (30 dpd, 60 dpd, etc.). Here we focus on the use of stochastic counting processes for mortgage default modeling, using data on high LTV mortgages. Borrowers seeking to finance more than 80% of a house's value with a mortgage usually either purchase mortgage insurance, allowing a first mortgage greater than 80% from many lenders, or use second mortgages. Are there differences in performance between loans financed by these different methods? We address this question in the counting process framework. In fact, MI is associated with lower default rates for both fixed rate and adjustable rate first mortgages.

Keywords: Econometrics; Aalen Estimator; Duration Modeling; Mortgage Insurance; Loan-to-Value (search for similar items in EconPapers)
JEL-codes: C33 C35 C51 C52 C58 (search for similar items in EconPapers)
Pages: 65
Date: 2015-04-28
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-ias, nep-rmg and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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