Using Structural Models for Default Prediction

62 Pages Posted: 22 Mar 2009

Multiple version iconThere are 2 versions of this paper

Date Written: March 18, 2009

Abstract

I propose a new procedure for extracting probabilities of default from structural credit risk models based on virtual credit spreads (VCS) and implement this approach assuming a simple Merton (1974) model of capital structure. VCS are derived from the increase in the payout to debtholders necessary to offset the impact of an increase in asset variance on the option value of debt and equity. In contrast to real-world credit spreads, VCS do not contain risk premia for default timing and recovery uncertainty, thus yielding a purer estimate of physical default probabilities. Relative to the Merton distance to default (DD) measure, my measure (i) predicts higher credit risk for safe firms and lower credit risk for firms with high volatility and leverage (ii) requires fewer parameter assumptions (iii) clearly outperforms the DD measure when used to predict corporate default.

Keywords: Structural Credit Risk Models, Bankruptcy Prediction, Risk-Neutral Pricing

JEL Classification: G33, G13, G32

Suggested Citation

Grass, Gunnar, Using Structural Models for Default Prediction (March 18, 2009). Available at SSRN: https://ssrn.com/abstract=1362519 or http://dx.doi.org/10.2139/ssrn.1362519

Gunnar Grass (Contact Author)

HEC Montréal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H3T2A7
Canada
5143401540 (Phone)

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
195
Abstract Views
874
Rank
90,984
PlumX Metrics