A Structural Approach for Predicting Default Correlation

20 Pages Posted: 6 Sep 2012 Last revised: 21 Jan 2013

See all articles by Sheen Liu

Sheen Liu

Washington State University - Vancouver

Howard Qi

Michigan Technological University; Independent

Jian Shi

Federal National Mortgage Association (Fannie Mae)

Yan Alice Xie

University of Michigan at Dearborn

Date Written: August 31, 2012

Abstract

Default correlation is a critical concept in risk management for fixed income investment, bank management, and insurance industry, working capital management, among many. We extend the Leland-Toft term structure model into a two-firm environment and predict the default correlation between two firms by directly simulating the calibrate model based on the observed equity data (1990-2010) for various ratings. Using our empirical default correlation estimation as the benchmark, our investigation sheds more light on the structural approach in predicting default correlation. The results show that our approach outperforms the previous Zhou’s model, thereby our approach is not only theoretical improvement, but also has an empirical advantage.

Keywords: default correlation, structural model, defaultable bonds, credit risk, fixed income, risk management

JEL Classification: G1, G2

Suggested Citation

Liu, Sheen and Qi, Howard and Shi, Jian and Xie, Yan, A Structural Approach for Predicting Default Correlation (August 31, 2012). Midwest Finance Association 2013 Annual Meeting Paper, Available at SSRN: https://ssrn.com/abstract=2140257 or http://dx.doi.org/10.2139/ssrn.2140257

Sheen Liu

Washington State University - Vancouver ( email )

14204 NE Salmon Creek Ave.
Vancouver, WA 98686
United States

Howard Qi (Contact Author)

Michigan Technological University ( email )

1400 Townsend Dr.
Houghton, MI 49931
United States

Independent ( email )

Jian Shi

Federal National Mortgage Association (Fannie Mae) ( email )

3900 Wisconsin Avenue, NW
Washington, DC 20016-2892
United States

Yan Xie

University of Michigan at Dearborn ( email )

Dearborn, MI
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
125
Abstract Views
1,202
rank
272,480
PlumX Metrics