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Liquidity risk, i.e., the likelihood that a swap can be “sold” (i.e., assigned) may affect swap prices. This article addresses the importance of liquidity risk as a factor in the valuation of swaps, which are subject to default risk. The author presents a model for pricing these swaps by incorporating a proxy for liquidity risk. Using the model, the author finds that the effects of liquidity risk may partially offset the effects of default risk.

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.

Prior research provides evidence that firms make accounting choices to avoid violation of debt covenant provisions and the resulting costs of technical default. We extend this research by asking why some firms refrain from making accounting policy changes when faced with costs of technical default. We considered two possible explanations. First, we hypothesise that these defaulting firms may lack the flexibility to make accounting changes. Second, we hypothesise that these defaulting firms may lack incentive to change accounting methods. Results confirm prior research and indicate that defaulting firms make more accounting changes than non‐defaulting firms. The decision by defaulting firms to change or not change accounting methods during the three years ending in the year of a technical default of debt covenants can be explained in part by the ability of the firm and by the incentives of the firm to make a change.

This study aims to provide additional insights by further investigating the governance aspects including board composition, risk monitoring and management by the board, ownership structures as well as the incentive compensation.

This study investigates the relationships between corporate governance, risk-taking behaviors and default risk by analyzing 78 publicly listed Japanese regional banks during the 2007-2008 crisis period.

Banks that were more diversified in the run-up to the crisis were associated with higher default risk during the crisis. Foreign shareholders may have prompted banks to engage in higher risk-taking activities in pursuit of higher returns, putting banks at a higher risk of default. On the other hand, board-level risk management committees may have mitigated the risks to protect firms from rising default. Finally, banks perceived to have better quality accounting information, by being audited by one of the Big 4 auditors, benefitted by mitigating price misevaluation and thus reducing default risk during the crisis.

Different from the majority of previous related studies on the relationship between governance and performance of stock returns, the current study focuses on the relationship between governance and default risk during the crisis which has a more direct link through which governance practices can affect risk-taking behaviors and thus the default risk during the crisis. In addition to examining conventional governance aspects, this study also focuses on the more relevant aspects of banks’ risk monitoring functions.

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 paper is to, first, analyse to what extent the default probability based on structural models provides additional information and that accounting ratios do not contemplate. Second, to design hybrid models by including the default probability from structural models as explanatory variable, in addition to accounting ratios, in order to evaluate the differences in the accuracy of default predictions using an accounting‐based model and a hybrid model.

The authors calculated the scores from the accounting models annually during the period from 2003 to 2007 and estimated several structural models.

The results show that the market information obtained from the structural models includes additional information not reflected in the accounting information. Also, it can be concluded that including default probability from structural models as an explanatory variable allows the out‐sample predictive capacity of accounting‐based models to be improved.

The study highlights the importance of combining a structural model with an accounting model rather than expending energy on determining which of the two provides a greater predictive capacity. In fact, recent literature demonstrates no superiority of one approach over the other because both approaches capture different aspects related to the risk of bankruptcy in companies and they should be combined to improve credit risk management.

This study expands on the existing literature on the probability of business failure in the real estate sector. The authors present a comparative analysis of the accuracy of default predictions using accounting‐based models and hybrid models which will consider the default probability implicit in market information.

The chapter studies strategic default using an experimental approach.

The experiment considers a stochastic asset process and a loan with no down-payment. The treatments are two asset volatilities (high and low) and the absence and presence of social interactions via a direct effect on the subject's payoff.

I demonstrate that (i) people appear to follow the prediction of the strategic default model quite closely in the high asset volatility treatment, and that (ii) incorporating social interactions delays the strategic default beyond what is considered optimal.

The study tests adequately the strategic default using a novel experimental design and analyzes the neighbor's effect on that decision.

The purpose of this paper is to focus on public guarantees granted to micro-, small- and medium-sized enterprises (SMEs) by the Italian national credit guarantee programme (Fondo Centrale di Garanzia – Central Guarantee Fund – (CGF)). The CGF provides a direct guarantee to banks granting loans or a counter-guarantee to mutual guarantee institutions (MGIs) acting as first-level guarantors. Because the behaviour of MGIs could affect the default risk of counter-guaranteed loans, it is vital to investigate their operating and structural characteristics in order to identify an optimal design for public credit guarantee schemes (PCGSs).

Using regression models, the paper analyses the determinants of default for 33,229 SME loans guaranteed by an MGI and counter-guaranteed by the Italian CGF. The dependent variable is the ex-post default risk of SMEs’ counter-guaranteed loans in the 2010-2011 period. The explanatory variables are certain characteristics of the MGI.

The authors demonstrate that increases in an MGI’s leverage and the size of the counter-guaranteed portfolios increase the default risk. When the counter-guaranteed portfolio increases, MGIs are more risk taking but take less risk than when local and specialized MGIs are at play. Finally, direct public aid is relevant.

An appropriate design of the PCGS becomes crucial to controlling moral hazard in financial institutions and ensuring the financial sustainability of public intervention in favour of SMEs.

The paper evaluates an original and confidential firm-level data set that is not available in public documents or supervisory board statistics but is collected directly from the MGIs that participated in this study.

The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

We set out, in this paper, to extend the Das and Sundaram (2000) model as a means of simultaneously considering correlated default risk structure and counter-party risk. The multinomial model established by Kamrad and Ritchken (1991) is subsequently modified in order to facilitate the development of a computational algorithm for valuing two types of active credit derivatives, credit-spread options and default baskets. From our numerical examples, we find that along with the correlated default risk, the existence of counter-party risk results in a substantially lower valuation of credit derivatives. In addition, we find that different settings of the term structure of interest rate volatility also have a significant impact on the value of credit derivatives.