Is the Distance to Default a Good Measure in Predicting Bank Failures? Case Studies

Harada, Kimie; Ito, Takatoshi; Takahashi, Shuhei

doi:10.3386/w16182

Cited by 27 publications

(26 citation statements)

References 13 publications

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“…Latter also mentions Swedish defaults. Germany and Japanese cases are discussed in (Mourlon-Druol, 2015) and (Harada, et al, 2010), respectively. Second, we browsed academic databases of EBSCO, JSTOR and search engines of Google, Yandex, Bing for the following key words: bank default, bank failure, banks losses, banks defaults, banks crisis.…”

Section: Methodsmentioning

confidence: 99%

History of the World Largest Financial Losses in 1972-2018

Henry

Mikhail

2018

SSRN Journal

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Deregulation is often claimed to be the cause for financial distress. Thus it has to lead to financial defaults and losses. However, exact dependence is not clear. To verify it we tried to investigate the roots of world largest financial losses. As we found no source to extensively and completely cover those, we decided to prepare a data set of our own. By choosing a round threshold of USD 100m equivalent of loss amount as of announcement date, we arrived at the set of 117 defaults with total loss of USD 914bn in 46 years. Our key purpose is to make comprehend data set of largest losses.

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Section: Methodsmentioning

confidence: 99%

History of the World Largest Financial Losses in 1972-2018

Henry

Mikhail

2018

SSRN Journal

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“…Since we aim to focus on country level risk measurement in EMU, we would aggregate the DtD at country level; and (2) How can individual banks' data be aggregated as a system-wide representation? Here we follow Saldias (2013), Harada and Ito (2008) and Harada et al (2010), and take the simple cross-sectional equal-weighted average at each point in time for all banks headquartered in a particular country as the aggregated risk measure. The simple average DtD for country i at time t is represented by aDtD i,t :…”

Section: Datamentioning

confidence: 99%

Bank risk behavior and connectedness in EMU countries

Singh

Gómez‐Puig

Sosvilla‐Rivero

2015

Journal of International Money and Finance

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Highlights1. We estimate the banking sector risk behavior in EMU over the period 2004Q4-2013Q2; 2. We compute average "Distance-to-default (DtD)" for a representative set of banks at country level;3. Average DtD is strongly correlated with market sentiments and outperform regulatory risk measures; 4. GC test reveal no systemic component and Diebold-Yilmaz index suggest low connectedness; 5. Results suggest more cautious requirement in modeling a priori dependence among bank. Abstract Comment [E1]:Author: Two different versions of Abstracts were provided and the one in the ms has been retained. Please check that is correct.Given the structural differences in banking sector and financial regulation at country level in European Economic and Monetary Union (EMU), this paper tries to estimate the banking sector risk behavior at country level. Based on contingent claim literature, it computes "Distance-to-default (DtD)" at bank level and analyses the aggregate series at country level for a representative set of banks over the period 2004-Q4 to 2013-Q2. The indices provide an intuitive, forward-looking and timely risk measure having strong correlations with national/regional market sentiment indicators. An underlying trend exists but causality tests suggest no systemic component. Cross-sectional differences in DtD suggests fragility in EMU countries 12-18 months prior to the crisis and better predictive ability than the regulatory index based on large and complex banking institutions at European level. Furthermore, we explore the reasons for this divergence using VAR estimates.JEL: G01 G13 G21 G28 IntroductionThe 2007-08 financial crisis and the subsequent European sovereign debt crisis have exacerbated the need to understand and monitor the bank risk behavior. Renewed attention is being focused at the global scale to enhance and extend risk measurement methodologies. The eurozone is no exception and the twin objective of the European Central Bank (ECB) -price and financial system stability -places a strong emphasis on Systemically Important Financial Institutions (SIFI) but relies on individual countries' central banks to supervise smaller financial institutions.This paper deviates from this current and in our view excessive focus and attention on detecting and monitoring risk at European banking level. We take a step backward and introduce a micro approach to document and monitor the buildup of banking sector risk at country level. Based on contingent claims literature, we calculate Distance-to-default (DtD) at bank level and analyze the aggregate series at country level for a representative set of banks over the period 2004-Q4 to 2013-Q2.Conceivably, if regulators pay greater attention to country-specific buildups of risk and their connectedness, they might take actions earlier to mitigate the extent and impact of future crisis.There are many reasons for this choice. First, the structure of the banking sector within EMU countries varies considerably. In the case of Germany, Finland and the Netherlands, total banking ...

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“…Several papers have examined the usefulness of DtD as a tool for predicting corporate and bank failure (Kealhofer (2003); Oderda et al (2003); Vassalou and Yuhang (2004); Gropp et al (2006); Harada et al (2010); Susan et al (2012)). They found DtD as a powerful measure to predict bankruptcy and rating downgrades.…”

Section: Literature Surveymentioning

confidence: 99%

“…We follow Harada and Ito (2008) and Harada et al (2010) which provided empirical evidence of DtD usefulness to detect bank default risks. The systemic risk indicator in this case was an average of individual DtD series.…”

Section: Literature Surveymentioning

confidence: 99%