Risk Assessment of Local Government Debt based on KMV Model: A Case Study of Hangzhou, Zhejiang Province

The present government debt governance focuses on calculating, preventing, and controlling local government debt risk. The default probability of local government debt in Hangzhou, Zhejiang Province, is calculated using a modified “Kealhofer, McQuown, and Vasicek” (KMV) model. The findings reveal that Hangzhou’s debt risk in the next three years is usually manageable, but that debt risk will progressively emerge in the coming years when the debt payback cycle begins.

Benchmark test for calculated default probability values derived from the application of rating-based models

Subject. This article deals with the issues of validation of the consistency of rating-based model forecasts. Objectives. The article aims to provide developers and validators of rating-based models with a practical fundamental test for benchmarking study of the estimated default probability values obtained as a result of the application of models used in the rating system. Methods. For the study, I used the classical interval approach to testing of statistical hypotheses focused on the subject area of calibration of rating systems. Results. In addition to the generally accepted tests for the correspondence of the predicted probabilities of default of credit risk objects to the historically realized values, the article proposes a new statistical test that corrects the shortcomings of the generally accepted ones, focused on "diagnosing" the consistency of the implemented discrimination of objects by the rating model. Examples of recognizing the reasons for a negative test result and negative consequences for lending are given while maintaining the current settings of the rating model. In addition to the bias in the assessment of the total frequency of defaults in the loan portfolio, the proposed method makes it possible to objectively reveal the inadequacy of discrimination against borrowers with a calibrated rating model, diagnose the “disease” of the rating model. Conclusions and Relevance. The new practical benchmark test allows to reject the hypothesis about the consistency of assessing the probability of default by the rating model at a given level of confidence and available historical data. The test has the advantage of practical interpretability based on its results, it is possible to draw a conclusion about the direction of the model correction. The offered test can be used in the process of internal validation by the bank of its own rating models, which is required by the Bank of Russia for approaches based on internal ratings.

Impact of the COVID-19 Pandemic on the US Credit Default Swap Market

The COVID-19 pandemic affected the US economy at different levels. Since credit default swaps can be viewed as a default probability indicator, the article shows the credit default swap market perspective on how the US economy was hit by the pandemic. Forecasting models are built to estimate the predictability of the CDS market sectors during the pandemic, i.e., manufacturing, energy, banks, consumer goods, and services and financial sector excluding banks. Econometric tests are applied to check the uniqueness of credit default swap market sectors after the declaration of the pandemic. The results indicate that the financial sector excluding banks performed uniquely during the pandemic; i.e., the predictability of this sector dropped significantly, and the Chow breakpoint test and Wald coefficient test can identify the shift in the data after declaration of the pandemic.

APPLICATION OF STATISTICAL TECHNIQUES IN THE PRACTICE OF BUILDING A DEFILIENCE SCALE

The main substantial features of the PD curve (default probability) formed in practical modeling are substantiated in the articles. It is proved that the main characteristics of the PD curve are that it is based on data on the actually restored default rate in each of the risk classes over a period of time and has a shape that approximate for coincides with the exposure function. It is shown that the best aspect that affects the calibration is the number of rating classes and ways to build them. It is determined that the slope of the curve demonstrates the classification model of efficiency. It is determined that the slope of the curve demonstrates the classification efficiency of the model. Models with high discriminant properties are characterized by a curve shape that has a slow increase in the rating classes of the upper part of the scale and a significant acceleration of growth in the last risk classes. Two main approaches to determining the number of risk classes are analyzed: the percentile-based approach and the equal score range approach. It is shown that when forming classes, it is necessary to take into account the total amount of sample observations, the proportion of «good» and «bad», and choose the number of classes so that it is not too large and not too small. Calibration practice shave been shown to be influenced by data, purpose, and study limitations. The application of the least squares method and the extrapolation method is considered on practical examples. The least squares method and in particular the derived extrapolation method allow to build a calibration curve on the basis of data on the relative frequency of defaults. It is determined that the mathematical apparatus of the family of nonlinear curves allows to model the process of exponential growth with different levels of intensity. The exponential curve and related functions may be useful in modeling more conservative PD estimates or for models with highly discriminatory properties, while the Weibull function, S-curve, and power function may be better adapted to moderate growth processes. The application of practical methods of constructing the PD scale is important for many domestic banking professionals who deal with internal models of credit risk. Keywords: Calibration, Default, Probability, Curves, Probability of default curve calibration, Least squares method, Extrapolation method. JEL Classіfіcatіon С44 Formulas: 21; fig.: 1; tabl.: 7; bibl.: 10.

Research, innovation, and bankruptcy: evidence from European manufacturing firms

This paper investigates the effects of R&D and innovation activities of firms on the risk of bankruptcy. The analysis is carried out on data drawn from the EU-EFIGE Survey and Amadeus Database (Bureau Van Dijk) on European manufacturing firms over the years 2009–2014. The empirical evidence shows that default probability is increasing in R&D investments and decreasing in innovation and productivity of research, measured by the ratio of innovation revenues to R&D outlays. In addition, to disentangle the influence of different innovation strategies (product, process, and patenting), 16 different firm profiles are compared. Firms carrying out R&D, adopting process innovation, and filing for patents are found to show the lowest probability of default. Sensitivity checks indicate that research and innovation do not affect the risk of financial distress events short of default (such as reorganization or application for insolvency procedure). Our findings are robust to potential endogeneity and hence allow for causal inference about the relationship between research and innovation and corporate bankruptcy.

Volatility morphology of asset value and credit spread puzzle

Merton model has provided a classic theoretical framework for explaining credit spreads. This paper extends Merton model by introducing morphology factor of asset value volatility in the model, and conducts empirical studies on the effect of asset volatility morphology on credit spreads in China’s bond market. The results show that asset volatility morphology is economically important and can explain credit spreads well. Furthermore, this paper analyzes the asymmetric influences of monetary policy on credit spreads and asset volatility morphology. This paper points out that the responses of credit spreads and asset volatility morphology to monetary policy are consistent in the tight liquidity environments. To this end, monetary policy and liquidity, which are two factors that have been ignored by classic Merton model but proved to have significant influences on credit spreads, play roles in influencing credit spreads by changing volatility morphology of asset value. Since asset volatility morphology can reflect the change of investors’ expectation on the default probability of asset, the argument mentioned in the credit spread puzzle that the fundamentals related to bond default probability cannot explain credit spreads needs to be reexamined.

Research on the Measurement of Subject Credit Risk of Chinese Port Enterprises by Constrained Logistic Regression

This paper studies the subject credit risk of Chinese port enterprises. Since the impact of cash flow ability on credit risk measurement will be increased under extreme case, ordinary logistic regression methods may lack explanatory power for port enterprise default under extreme cases. Considering the characteristics of cash flow in port industry, we introduce the constrained logistic regression method to establish a default probability model which can describe the credit risk level of the industry with higher accuracy in the extreme case where an enterprise’s quick ratio is lower than a cutoff point, For empirical study, we leverage the data of more than 900 companies in port and transportation industry in 2016–2018. The constrained logistic regression splits the data into two subspaces based on quick ratios with the cutoff of 1.8. Then logistic regression is built on the two subspaces, respectively. The recall ratios show that the constrained logistic regression method performs better than the ordinary logistic regression on the study of corporate default probability in port and transportation industry.