Department of Economics and Business Economics

Counting Processes for Retail Default Modeling

Research output: Working paper/Preprint Working paper

Standard

Counting Processes for Retail Default Modeling. / Kiefer, Nicholas Maximilian; Larson, C. Erik.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2015.

Research output: Working paper/Preprint Working paper

Harvard

Kiefer, NM & Larson, CE 2015 'Counting Processes for Retail Default Modeling' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Kiefer, N. M., & Larson, C. E. (2015). Counting Processes for Retail Default Modeling. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2015-17

CBE

Kiefer NM, Larson CE. 2015. Counting Processes for Retail Default Modeling. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Kiefer, Nicholas Maximilian and C. Erik Larson Counting Processes for Retail Default Modeling. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2015-17). 2015., 65 p.

Vancouver

Kiefer NM, Larson CE. Counting Processes for Retail Default Modeling. Aarhus: Institut for Økonomi, Aarhus Universitet. 2015 Apr 28.

Author

Kiefer, Nicholas Maximilian ; Larson, C. Erik. / Counting Processes for Retail Default Modeling. Aarhus : Institut for Økonomi, Aarhus Universitet, 2015. (CREATES Research Papers; No. 2015-17).

Bibtex

@techreport{6e67cfb1b1a94bf7b628f735329eebae,
title = "Counting Processes for Retail Default Modeling",
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 d",
keywords = "Econometrics, Aalen Estimator, Duration Modeling, Mortgage Insurance, Loan-to-Value",
author = "Kiefer, {Nicholas Maximilian} and Larson, {C. Erik}",
year = "2015",
month = apr,
day = "28",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2015-17",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Counting Processes for Retail Default Modeling

AU - Kiefer, Nicholas Maximilian

AU - Larson, C. Erik

PY - 2015/4/28

Y1 - 2015/4/28

N2 - 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 d

AB - 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 d

KW - Econometrics, Aalen Estimator, Duration Modeling, Mortgage Insurance, Loan-to-Value

M3 - Working paper

T3 - CREATES Research Papers

BT - Counting Processes for Retail Default Modeling

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -