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This study aims to compare two distance-to-default methods, data-transformed maximum likelihood estimation and “naïve”, that are suitable for financial institutions. The links between these measures and asset size, Tier 1 and Tier 2 capital ratios, non-performing assets and operating efficiency 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.

The author has compared two distance-to-default measures and has identified their accounting determinants using Pearson’s correlation and regressions with clustered standard errors. The sample of the US-based systemically important financial institutions covers the period from 2000 to 2010.

Non-performing assets and operating efficiency are found to be statistically and economically significant determinants of both distance-to-default measures. Tier 1 capital ratio is not a significant indicator of default risk.

The results emphasize the importance of using a combination of market-based default risk measures and accounting ratios in default prediction models for the financial institutions.

This paper identifies accounting determinants of two distance-to-default measures for large financial institutions, before and during the 2008 financial crisis. It introduces a spread between two measures as an alternative default risk indicator.

An investor in a corporate obligation is exposed to the default risk of the obligor. In this article, the author adapts the dynamic valuation framework to disaggregate systematic and idiosyncratic default risk of credit instruments. By articulating the distinction between diversifiable and undiversifiable risk, the article develops a two‐factor model for pricing default risk.

The main purpose of this study is to evaluate the probability of default and examine the relationship between default risk and financial performance, with dynamic panel moderation of firm size.

This study utilizes a total of 1,500 firm-year observations from 2013 to 2018 using dynamic panel data approach of generalized method of moments to test the relationship between default risk and financial performance with the moderation effect of the firm size.

This study establishes the findings that default risk significantly impacts the financial performance. The relationship between distance-to-default (DD) and financial performance is positive, which means the relationship of the independent and dependent variable is inverse. Moreover, this study finds that the firm size is a significant positive moderator between DD and financial performance.

This study provides new and useful insight into the literature on the relationship between default risk and financial performance. The results of this study provide investors and businesses related to nonfinancial firms in the Pakistan Stock Exchange (PSX) with significant default risk's impact on performance. This study finds, on average, the default probability in KSE ALL indexed companies is 6.12%.

The evidence of the default risk and financial performance on samples of nonfinancial firms has been minimal; mainly, it has been limited to the banking sector. Moreover, the existing studies have only catered the direct effect of only. This study fills that gap and evaluates this relationship in nonfinancial firms. This study also helps in the evaluation of Merton model's performance in the nonfinancial firms.

The purpose of this paper is to develop a hybrid logistic model by using the inputs obtained from BSM equity‐based option model described in the companion paper, “Mapping corporate drift towards default – Part 1: a market‐based approach” that can more accurately predict corporate default.

In a set of logistic regressions, the ability of the market value of assets, asset volatility and firm's leverage structure measures to predict future default is investigated. Next, a check is made as to whether accounting variables and other firm specific characteristics can provide additional significant information in assessing the real world credit quality of a firm in a multifactor model

From analysis of 150 publicly‐traded Indian corporates over the year 1998 to 2005 it was found that in a volatile equity market like India, one needs to enhance the BSM model with other accounting information from financial statements and develop hybrid models. The results in this paper indicate that a mix of asset volatility, market value of asset and firm's leverage structure along with other financial and non financial factors can give us a more accurate prediction of corporate default than the ratio‐based reduced form model.

The hybrid model developed in this paper allows us to integrate information from the structural model as well as profitability of firms, liquidity risk, other firm specific supplementary information and macroeconomic factors to predict real world corporate distress potential through a multivariate analysis.

Motivated by lack of empirical research on sukuk (Islamic bonds) defaults and factors influencing the credit risk in sukuk industry, the study investigates the impact of corporate governance (CG) practices and corporate social sustainability (CS) disclosures on default risk of Islamic bonds in an emerging market.

In the Malaysian context the authors use generalized method of moments (GMM) to examine the mitigating effect of CG structure and CS disclosures on distance to default (DD) of sukuk issuers.

The results show that although both CG and CS have a significant and positive relationship with distance to default, the contribution of CS to augment DD is higher. Moreover, different CG variables have a varied relationship with distance to default, while the association is positive for all three pillars of CS, videlicet economic, social and environmental sustainability.

The findings of the study hold important implications for issuers, subscribers and regulators in the sukuk industry.

Limited research investigates the relationship between CG, CS and default risk of Islamic bonds. In light of this, the study attempts to fill the theoretical void in literature by examining the relationship among the underlying variables.

This study aims to render a fundamental assessment of the Basel II internal ratings-based (IRB) approach by taking readings of the Australian banking sector since the implementation of Basel II and comparing them with signals from macroeconomic indicators, financial statistics and external credit ratings. The IRB approach to capital adequacy for credit risk, which implements an asymptotic single risk factor (ASRF) model, plays an important role in protecting the Australian banking sector against insolvency.

Realisations of the single systematic risk factor, interpreted as describing the prevailing state of the Australian economy, are recovered from the ASRF model and compared with macroeconomic indicators. Similarly, estimates of distance-to-default, reflecting the capacity of the Australian banking sector to absorb credit losses, are recovered from the ASRF model and compared with financial statistics and external credit ratings. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-2009 was most acutely felt, the authors measure the impact of the crisis on the Australian banking sector.

Measurements from the ASRF model find general agreement with signals from macroeconomic indicators, financial statistics and external credit ratings. This leads to a favourable assessment of the ASRF model for the purposes of capital allocation, performance attribution and risk monitoring. The empirical analysis used in this paper reveals that the recent crisis imparted a mild stress on the Australian banking sector.

Given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, the authors cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment.

Access to internal bank data collected by the prudential regulator distinguishes this paper from other empirical studies on the IRB approach and financial crisis of 2007-2009. The authors are not the first to attempt to measure the effects of the recent crisis, but they believe that they are the first to do so using regulatory data.

The ownership structure in Japanese firms has experienced a significant change recently, fueled primarily by regulatory changes. This has important repercussions on corporate performance and risk. This paper examines the impact of insider ownership on the default risk of Japanese firms.

We collected data from the Nikkei Corporate Governance Evaluation System (CGES) database for the period 2004–2019. Our final dataset yields 36,116 firm-year observations. We apply a firm fixed effect model for baseline regression. Endogeneity was checked by applying propensity score matching (PSM) and two-stage least squares (2SLS) techniques. Furthermore, the robustness of baseline regression results was checked using alternative estimation techniques.

Results show a significant positive influence of insider ownership on default risk. Furthermore, ROA volatility and stock price volatility appear to be the major channels through which insider ownership affects a firm’s default risk. We further document that external monitoring mechanisms, including traditional main bank ties, institutional ownership and analyst coverage, are the key risk-mitigating factors.

Our research deals with Japanese firms only. Future research may attempt to analyze the cases of emerging economies. Furthermore, future research might examine the ownership-default risk relationship for financial institutions to see if this relationship differs between financial and nonfinancial firms.

Insider ownership enhances the probability of default. Hence, policymakers may consider instituting a ceiling for insider ownership in Japanese firms. Moreover, we highlight various risk-mediating channels that would help policymakers adopt guidelines for mitigating corporate risk.

Our study is the first to investigate the effect of insider ownership on default risk in Japanese settings. Prior studies identified various determinants that affect firms’ default risk. Our study contributes to this stream of literature by examining the impact of insider ownership on default risk and extending the limited literature related to insider ownership.

The credit migration process contains important information about the dynamics of a firm's credit quality, therefore, it has a significant impact on its relevant credit derivatives. We present a jump diffusion approach to model the credit rating transitions which leads to a partial integro-differential equation (PIDE) formulation, with defaults and rating changes characterized by barrier crossings. Efficient and reliable numerical solutions are developed for the variable coefficient equation that result in good agreement with historical and market data, across all credit ratings. A simple adjustment in the credit index drift converts the model to be used in the risk-neutral setting, which makes it a valuable tool in credit derivative pricing.

The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies?

The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models.

The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models.

The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information.

Better forecasts demand a mix of models containing both accounting and market information.

The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

This paper uses contingent claims analysis to evaluate the implicit government guarantee to Fannie Mae and Freddie Mac prior to their placement into conservatorship. The main findings of the paper indicate that the expected value of the guarantee was in line with the size of capital injections under the Treasury Preferred Stock Purchase Agreement and that the market expected the government to cover nearly all expected losses on senior debt. However, simulations reveal that the eventual total cost to recapitalize the GSEs may be significantly higher than provided for under the original terms of the conservatorship.