Estimating Structural Bond Pricing Models

70 Pages Posted: 15 May 2001

See all articles by Jan Ericsson

Jan Ericsson

McGill University; Swedish Institute for Financial Research (SIFR)

Joel Reneby

Stockholm School of Economics - Department of Finance

Multiple version iconThere are 2 versions of this paper

Date Written: March 2002

Abstract

A difficulty which arises when implementing structural bond pricing models is the estimation of the value and risk of the firm's assets - neither of which is directly observable. We perform a simulation experiment in order to evaluate a maximum likelihood method applicable to this problem. The properties of the bond price estimators are examined using four theoretical bond pricing models: the Black & Scholes (1973) / Merton (1974) model, the Leland & Toft (1996) model, the Briys & de Varenne (1997) model, as well as the Ericsson & Reneby (2001) model. We contrast the performance of the maximum likelihood estimators to that of estimators traditionally used in academia and industry. The results are strongly supportive of the maximum likelihood approach. In fact, the inefficiency of the traditional estimator may explain the failure of past attempts to implement structural bond pricing models.

Keywords: Credit Risk, Maximum Likelihood, Corporate Bonds

JEL Classification: G12, G13

Suggested Citation

Ericsson, Jan and Reneby, Joel, Estimating Structural Bond Pricing Models (March 2002). Available at SSRN: https://ssrn.com/abstract=268786 or http://dx.doi.org/10.2139/ssrn.268786

Jan Ericsson (Contact Author)

McGill University ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada
(514) 398-3186 (Phone)
(514) 398-3876 (Fax)

HOME PAGE: http://people.mcgill.ca/jan.ericsson/

Swedish Institute for Financial Research (SIFR)

Drottninggatan 89
SE-113 59 Stockholm, SE-113 60
Sweden

Joel Reneby

Stockholm School of Economics - Department of Finance ( email )

SE-113 83 Stockholm
Sweden
+46 8 7369143 (Phone)
+46 8 312327 (Fax)

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

Paper statistics

Downloads
2,568
Abstract Views
8,221
rank
7,713
PlumX Metrics