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This paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.

This paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.

This study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.

Many issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.

The proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.

No previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.

This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient.

Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model.

The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons.

The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

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 purpose of this paper is to examine the factors influencing defaults and exits from public works by prime contractors of small and medium-sized enterprises in the Japanese construction industry. By analysing the data for several years as panel data, this study determines the extent of influence of changes in company characteristics on the defaults and exits.

Using construction company evaluation (Keiei Jikou Sinsa or Keisin) data and by employing the panel binary logit random effect model, this study empirically analyses the construction industry.

This study shows that defaulting and exiting companies have different characteristics. The result shows financial performance indicators, non-financial performance indicators and Keisin scores to significantly affect defaults and exits. In particular, this study finds non-financial performance indicators, such as whether a firm draws insurance, to significantly affect its likelihood to default or exit and the influence varies on the basis of insurance type.

The feature of this study is that its analysis focuses not only on defaulting companies but also on exiting companies, defined as those that stop operating as prime contractors for public works but otherwise stay in business. In contrast to existing research, this study distinguishes between defaults and exits and analyses the factors that influence a firm following one of these two outcomes. Moreover, although Keisin data are usually used to determine whether companies qualify to enter bids for public works, they can be applied for an attribution analysis of corporate defaults and exits.

The purpose of this study is to examine what factors affect the exit of small and medium-sized enterprises (SMEs) from tendering for public works in the Japanese construction industry using the Keiei Jikou Sinsa or Keisin (the database for evaluation of construction companies in Japan).

This study empirically analyzes SMEs’ exit using the binary logit model. For the empirical analysis, it uses the scores as well as financial and non-financial performance indicators of Keisin data.

The Keisin scores (the total score and W score), financial performance indicators (cash flow from operations and capital) and non-financial performance indicators (having unemployment insurance and operating years) significantly affect SME exits. Although the Keisin data are used for bid entry qualifications of public works, they can be applied to a factor analysis of the exit of SMEs in the construction industry.

As there exists little empirical analysis of the exit of SMEs globally, this study contributes to the research on this phenomenon.

This paper estimates the conditional hazard baseline (term-structure) of the hazard rate to default at the time of bonds’ issuance by using two hazard models–one ignoring and another allowing unobserved heterogeneity (UH) in the hazard rate. Following Diamond (1989) one can predict a declining hazard rate to default due to adverse selection and moral hazard. After controlling for UH caused by adverse selection and time-series shocks, the hazard rate shows to be increasing over time and hence the moral hazard effect cannot be confirmed.

This paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations.

The author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error.

The models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations.

The tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements.

Research findings are useful for practitioners, who apply and develop the ECL models of financial accounting.

The innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.

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.

This article uses a Kalman filter to fit yields of investment-grade corporate bonds to the model of instantaneous default risk, based on Duffee (1999. Review of Financial Studies. 12. PP. 197-226). The first part of this article fits the term structure of default-free interest rates to a translated two-factor square-root diffusion model. The parameters in the two-factor model are estimated by using a quasi-maxirnum-likelihood estimator in a state-space model in the Korean treasury bond market. A Kalman filter is used to estimate the unobservable factors.

The two-factor model successfully incorporates random variations in the slope of the term structure and the level of interest rates‘ After estimating the default-free term structure of interest rates, the second part of this article extends the model to noncallable corporate bonds‘ This is done by assuming that the probability of default follows a translated square-root diffusion process with the possibility of being correlated with default-free interest rates. The parameters of the process are estimated for investment-grade corporate bonds including AM. AA, A. and BBB. Empirical results show that the default risk is negatively correlated with default-free interest rates and confirm that the default risk is greater for lower grades. In addition, the estimated model successfully produces the term structures of credit spreads for corporate bonds and show that the credit spreads for lower grade bonds are more steeply sloped than those for higher grade bonds. These results show that Duffee's model can reasonably account for the observed corporate bond prices in the Korean bond market.

In this second installment, the author addresses some of the problems associated with empirically validating contingent‐claim models for valuing risky debt. The article uses a simple contingent claims risky debt valuation model to fit term structures of credit spreads derived from data for U.S. corporate bonds. An essential component to fitting this model is the use of expected default frequency; the estimate of the firms' expected default probability over a specific time horizon. The author discusses the statistical and econometric procedures used in fitting the term structure of credit spreads and estimating model parameters. These include iteratively reweighted non‐linear least squares are used to dampen the impact of outliers and ensure convergence in each cross‐sectional estimation from 1992 to 1999.