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This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables.

Multiple Discriminate Analysis (MAD) is used for developing Z‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default.

The new Z‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk.

Using the new Z‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

Estimation of default and asset correlation is crucial for banks to manage and measure portfolio credit risk. The purpose of this paper is to find empirical relationship between the default and asset correlation with default probability, to understand the effect of systematic risk.

The authors have estimated single default and implicit asset correlations for banks and corporates in India and compare it with global scenario. This paper deduces a simple methodology to estimate the default correlations from the variance of temporal default rates. Next, the asset correlations have been estimated analytically by decomposition of variance equation in Merton's one factor risk model following approaches of Gordy and of Bluhm and Overbeck.

The authors empirically find a negative relationship between asset correlation and the probability of default using Moody's global corporate data that support Basel II internal ratings‐based (IRB) correlation prescription. However, they do not find any smooth relationship between the probability of default (PD) and asset correlation for Indian corporate. The magnitude of correlation estimates based on a large bank's internal rating‐wise default rates are much lower than what is prescribed by the Basel committee. Thus, the standardized correlation figures as assumed by the Basel Committee on Banking Supervision need to be properly calibrated by the local regulators before prescribing their banks to calculate IRB risk weighted assets.

These correlation estimates will help the regulators, insurance firms and banks to understand the linkage between counterparty default risks with the systematic factors. The findings of this paper could be used further in estimating portfolio economic capital for large corporate exposures of banks and insurance companies.

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.

The purpose of this article is to discuss a Black‐Scholes‐Merton (BSM)‐based market approach to quantify the default risk of publicly‐listed individual companies.

Using the contingent claim approach, a framework is presented to optimally use stock market and balance sheet information of the company to predict its probability of failure as well as ordinal risk ranking over a horizon of one year.

By applying the methodology, yearly estimates of the risk neutral and real probability of default for 150 Indian corporates from 1998 to 2005 were constructed, that give up‐to‐date point‐in‐time perspective of their risk assessment. It was found that option model can provide ordinal ranking of companies on the basis of their default risk which also has good early warning predictability.

The option‐based default probability estimation may be an innovative approach for measuring and managing credit risk even in the emerging market economy. The asset value model developed in this paper based on the BSM model can facilitate the Indian banks as well as investors to get an early warning signal about the company's default status.

This paper aims to analyse the corporate rent-vs-buy decision on real estate through the trade-off theory and default option in the framework of a corporation that aims to optimise its capital structure.

The methodological core of this paper comprises the trade-off theory that approaches the optimal capital structure by counterbalancing debt tax savings with bankruptcy costs. Impacts on the default option and the default barrier are made explicit. The paper also explores the practical applicability of the renting scenarios in the European context by examining the regimes of real estate investment trusts in different countries from the demand-side of commercial renting.

Analytical relationships with tax savings, bankruptcy costs, default option and default barrier are identified for the renting-vs-buying real estate decisions.

The theoretical model assumes simplifications, such as constant debt, to make it operational. The paper centres exclusively on the trade-off capital structure theory.

This paper is an analysis of corporate real estate decisions together with capital structure. Applications are not only quantitative but also conceptual and strategic.

Identifying the main variables that govern the impact of corporate real estate decisions on capital structure and interweaving different approaches generates a conceptual framework that enlightens strategic thinking in this field.

The purpose of this paper is to estimate the effects of liquidity on corporate bond spreads.

Using a systematic liquidity factor extracted from the yield spreads between on- and off-the-run Treasury issues as a state variable, the authors jointly estimate the default and liquidity spreads from corporate bond prices.

The authors find that the liquidity factor is strongly related to conventional liquidity measures such as bid-ask spread, volume, order imbalance, and depth. Empirical evidence shows that the liquidity component of corporate bond yield spreads is sizable and increases with maturity and credit risk. On average the liquidity spread accounts for about 25 percent of the spread for investment-grade bonds and one-third of the spread for speculative-grade bonds.

The results show that a significant part of corporate bond spreads are due to liquidity, which implies that it is not necessary for credit risk to explain the entire corporate bond spread.

The results show that returns from investments in corporate bonds represent compensations for bearing both credit and liquidity risks.

It is a novel approach to extract a liquidity factor from on- and off-the-run Treasury issues and use it to disentangle liquidity and credit spreads for corporate bonds.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

This article surveys available research on the contingent‐claims approach to risky debt valuation. The author describes both the structural and reduced form versions of contingent claims models and summarizes both the theoretical and empirical research in this area. Relative to the progress made in the theory of risky debt valuation, empirical validation of these models lags far behind. This survey highlights the increasing gap between the theoretical valuation and the empirical understanding of risky debt.

The purpose of this study is to examine the relationship between corporate social responsibility (CSR) and the default risk level of firms operating in the Eurozone and how CSR can provide insurance-like protection during financial/economic downturns.

Based on prior empirical studies and by integrating the insights of different theories, this study proposes a framework linking CSR, firm default risk and corporate financial performance to explain firms' social behavior that can trigger default risk determinants (e.g. cost of capital, leverage, sales level) directly or indirectly. The authors use a panel regression approach.

The results support the mitigating effect of CSR on firm default risk. This effect is higher during a financial crisis, suggesting that CSR could provide insurance-like protection during economic downturns. These results hold even after using an alternative risk measure. Granger causality test results strongly suggest that reverse causality is not a concern. An instrumental variable approach is proposed to deal with potential endogeneity issues.

While other studies examine the CSR–firm default risk relationship in US samples, this study focuses on the Eurozone. The novelty of this work is based on its sample and how financial crises are addressed within this relationship. Insurance-like protection concerns both negative announcements and periods (e.g. financial crises, recessions). The study's results are useful for investors and risk managers who intend to manage default risk in their portfolios or firms.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.