Merton for Dummies: A Flexible Way of Modelling Default Risk
No 112, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney
One of the most popular approaches to default probability estimation using market information is the Merton  approach. By explicitly modelling a firm's market value, market value volatility and liability structure over time using contingent claims analysis the Merton model defines a firm as defaulted when the firm's value falls below its debt. In this paper we show how a simplified "spread sheet" version of the Merton model produces distance to default measures similar to the original Merton model. Moreover, when applied to a sample of US firms, the simplified model gives a relative ranking of firms that is essentially unchanged compared to the Merton model. Our paper has three main implications. First, the simplicity of our model makes it suitable as a framework for a more elaborate dynamic modelling of volatility and leverage ratios with the aim of capturing the dynamic nature of default risk suggested by empirical evidence. At the same time, in the model's most simple version, distance to default can be calculated very quickly and intuitively (on the back of an envelope). Second, the default probability's insensitivity to the leverage ration at high levels of debt makes it possible to apply the model to banks and other highly leveraged firms without exact knowledge of their leverage ratios. Third, the model can be applied to any firm regardless of its level of riskiness without estimation problems.
New Economics Papers: this item is included in nep-fin
References: Add references at CitEc
Citations View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:112
Access Statistics for this paper
More papers in Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().