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The credit ratings issued by the Big 3 ratings agencies are inaccurate and slow to respond to market changes. This paper aims to develop a rigorous, transparent and robust credit assessment and rating scheme for sovereigns.

This paper develops a regression-based model using credit default swap (CDS) data, and data on financial and macroeconomic variables to estimate sovereign CDS spreads. Using these spreads, the default probabilities of sovereigns can be estimated. The new ratings scheme is then used in conjunction with these default probabilities to assign credit ratings to sovereigns.

The developed model accurately estimates CDS spreads (based on RMSE values). Credit ratings issued retrospectively using the new scheme reflect reality better.

This paper reveals that both macroeconomic and financial factors affect both systemic and idiosyncratic risks for sovereigns.

The developed credit assessment and ratings scheme can be used to evaluate the creditworthiness of sovereigns and subsequently assign robust credit ratings.

The transparency and rigor of the new scheme will result in better and trustworthy indications of a sovereign’s financial health. Investors and monetary authorities can make better informed decisions. The episodes that occurred during the debt crisis could be avoided.

This paper uses both financial and macroeconomic data to estimate CDS spreads and demonstrates that both financial and macroeconomic factors affect sovereign systemic and idiosyncratic risk. The proposed credit assessment and ratings schemes could supplement or potentially replace the credit ratings issued by the Big 3 ratings agencies.

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 Covid-19 pandemic has rekindled interest in sovereign debt crises amidst calls for debt relief for developing and emerging countries. But has debt relief lessened the debt burdens of emerging and developing economies? The purpose of this paper is to empirically address this question. In particular, the focus is on the implications of debt relief and institutional qualities for sovereign debt in emerging and developing economies.

The model extends the framework on the probability of default by incorporating the receipt of debt relief by a debtor country. Doing so allows to better explain movements of sovereign defaults relating to debt relief. The model is estimated via the regular probit regression.

The analysis shows that the debt relief provided, thus, far, failed to ease the debt overhang problems of developing and emerging countries and reduced investment. The current debt relief schemes may underscore the prospects of self-enforcing and self-fulfilling sovereign debt crises rather than eliminating the dilemma completely. Regarding the forms of debt relief, the analysis shows that debt forgiveness offers favourable prospects in terms of debt sustainability and economic outcomes than debt rescheduling. Perhaps, the sovereign debt crises, particularly in low-income countries, hinge on insolvency problems rather than transitory illiquidity issues.

Any debt relief mechanism should consider seriously the potential incentive effect that reinforces expectations of future debt-relief initiatives. Importantly, solving the sovereign debt problem requires a programme for sustained investment and economic growth, while not discounting the critical role of prudent debt management policies and institutions.

This study contributes a different angle to the debate on sovereign debt distress. Aside from the structural and economic factors, this study investigates the role of debt management policy in the debtor nation and the implications of debt relief benefits for sovereign risk. The framework also focuses on whether the different forms of debt relief exert distinctive impacts.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

This paper proposes a method that estimates credit ratings by mapping empirical probability of default (PD) and standardized historical financial ratios. Unlike standard approaches such as the parametric logit model. discriminant analysis. neural network. and survival function model. the proposed approach has an advantage of offering a multiple credit rating categories. as opposed to either default or not default. of obligors. It would provide an useful information to practitioners because the probability of default for each credit rating category is a critical input under New Basel Capital Accord. Emoirical results based upon the historical PD and financial ratios of Korean savings bank industry from 2000 and 2003 suggest that the industry’s average credit rating belong to a speculative grade. that is BB and below.

In addition, the computed transition matrix indicates that volatility of transition matrix fluctuates substantially each year and the orobability of staying in the same rating category at the end of the year tended to be much smaller than the average reported by the rating agencies for the overall Korean companies. The proposed method can easily be applied to industries other than savings bank industry.

This study aims to develop a high-quality impairment loss allowance model in conformity with Indonesian Financial Accounting Standards 71 (PSAK 71) that has significant contribution to national interests and the banking industry.

The determination of the impairment loss allowance model is settled through 7 stages, using integration of some statistical methods such as Markov chain, exponential smoothing, time series analysis of behavioral inherent trends of probability of default, tail conditional expectation and Monte Carlo simulation.

The model which is developed by the authors is proven to be a high-quality and reliable model. By using the model, it can be shown that the implementation of the expected credit losses model on Indonesian Financial Accounting Standards 71 is more prudent than the implementation of the incurred loss model on Indonesian Financial Accounting Standards 55.

Determination of defaults was based on days past due, and the analysis in this study did not touch the aspects of hedge accounting in general.

This developed model will contribute significantly to national interests as a source of reference for other banks operating in Indonesia in calculating impairment loss allowance (CKPN) and can be used by the Financial Services Authority of Indonesia (OJK) as a guideline in assessing the formation of impairment loss allowance for banks operating in Indonesia.

As so far there is not yet an available standardized model for calculating impairment loss allowance on the basis of Indonesian Financial Accounting Standards 71, the model developed by the authors will be a new breakthrough in Indonesia.

The purpose of this paper is to study the evolution of financial contagion between Eurozone banks, observing the credit default swaps (CDSs) market during the period 2009–2017.

The authors use a dynamic spatial Durbin model that enables to explore the direct and indirect effects over the short and long run and the transmission channels of the contagion.

The results show how contagion emerges through physical and financial market links between banks. This finding implies that a bank can fail because people expect other related financial institutions to fail as well (self-fulfilling crisis). The study provides statistically significant evidence of the presence of credit risk spillovers in CDS markets. The findings show that equity market dynamics of “neighbouring” banks are important factors in risk transmission.

The research provides a new contribution to the analysis of EZ banking risk contagion, studying CDS spread determinants both under a temporal and spatial dimension. Considering the cross-dependence of credit spreads, the study allowed to verify the non-linearity between the probability of default of a debtor and the observed credit spreads (credit spread puzzle). The authors provide information on the transmission mechanism of contagion and, on the effects among the largest banks. In fact, through the study of short- and long-term impacts, direct and indirect, the paper classify banks of systemic importance according to their effect on the financial system.

The purpose of this study is to shed light on the relationship between patent applications and long-term risk for small firms across the global financial crisis of 2008. During a crisis, firm risk often skyrockets, and small and medium enterprises face significant dangers to their business continuity. However, managers have a set of strategies that could be implemented to increase a firm’s resilience, sustaining competitive advantages and improving access to financial resource. The authors focused on the investigating the impact of patenting activities on small business risk in a time of crisis.

This is a quantitative study based on a sample of Italian firms that applied for a patent in 2005. The changes in corporate credit ratings over a five-year period are related to different proxies of patent activity using multivariate regression analysis.

Firms that filed for a patent were more resilient, compared to the control sample, during the financial crisis. Innovative activities resulting in patent application seem to deliver strategic resources useful to tackle the crisis rather than increase riskiness. The moderating effect of patents on risk sensitivity is stronger for small firms and when the number of patents or the patent intensity is larger.

Limited evidence is available on how patent applications are related to risks for small firms during an economic crisis. The authors highlight that the innovative efforts resulting in patent applications can support small business resilience. The authors also point out that the implementation of patent information in small firms' credit score modeling is still an uncommon practice, while it is useful in estimating firm risk in a way more robust to exogenous credit shocks.