Using Structural Models for Default Prediction

61 Pages Posted: 26 Nov 2008

Multiple version iconThere are 2 versions of this paper

Date Written: November 25, 2008

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

Suggested Citation

Grass, Gunnar, Using Structural Models for Default Prediction (November 25, 2008). Available at SSRN: https://ssrn.com/abstract=1307059 or http://dx.doi.org/10.2139/ssrn.1307059

Gunnar Grass (Contact Author)

HEC Montréal ( email )

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

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