Distance-to-default measures and determinants for systemically important financial institutions
| Published date | 06 May 2014 |
| DOI | https://doi.org/10.1108/JFRC-02-2013-0004 |
| Pages | 159-172 |
| Date | 06 May 2014 |
| Author | Natalya A. Schenck |
| Subject Matter | Accounting & Finance,Financial risk/company failure,Financial compliance/regulation |
Distance-to-default measures
and determinants for
systemically important
nancial institutions
Natalya A. Schenck
Kent State University, Kent, Ohio, USA
Abstract
Purpose – This study aims to compare two distance-to-default methods, data-transformed maximum
likelihood estimation and “naïve”, that are suitable for nancial institutions. The links between these
measures and asset size, Tier 1 and Tier 2 capital ratios, non-performing assets and operating efciency have
been examined and an alternative default risk measure has been introduced. Most of the market-based
distance-to-default measures are not appropriate for banks due to their unique debt structure.
Design/methodology/approach – The author has compared two distance-to-default measures and
has identied their accounting determinants using Pearson’s correlation and regressions with clustered
standard errors. The sample of the US-based systemically important nancial institutions covers the
period from 2000 to 2010.
Findings – Non-performing assets and operating efciency are found to be statistically and
economically signicant determinants of both distance-to-default measures. Tier 1 capital ratio is not a
signicant indicator of default risk.
Practical implications – The results emphasize the importance of using a combination of market-based
default risk measures and accounting ratios in default prediction models for the nancial institutions.
Originality/value – This paper identies accounting determinants of two distance-to-default
measures for large nancial institutions, before and during the 2008 nancial crisis. It introduces a
spread between two measures as an alternative default risk indicator.
Keywords Financial crisis, Banks, Distance-to-default, Default risk
Paper type Research paper
1. Introduction
Financial institutions with their unique debt structure and specic regulatory
environment are often excluded from the studies involving standard default prediction
models. Given the paramount importance of the nancial institutions in the national and
global economy, it is essential to understand the market-based default prediction models
suitable for banks. Market-based distance-to-default measure is a default risk measure
based on the option pricing model of Black and Scholes (1973), structural model of
Merton (1974) or some variations of these models.
In this study, I have compared two market-based distance-to-default measures that
can be applied to banks: the computationally sophisticated data transformed maximum
likelihood estimation (MLE) method of Duan (1994,2000) and Duan and Wang (2012)
and the “naïve” approach suggested by Byström (2006). It had been shown that the
JEL classication – G21, G28, G32
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1358-1988.htm
Distance-to-default
measures and
determinants
159
Journal of Financial Regulation and
Compliance
Vol. 22 No. 2, 2014
pp. 159-172
© Emerald Group Publishing Limited
1358-1988
DOI 10.1108/JFRC-02-2013-0004
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