Credit Risk in G20 Nations: A Comparative Analysis in International Finance Using Option-Adjusted-Spreads

Corporate bond yields are the manifestation of the cost of financing for private firms, and if properly evaluated, they provide researchers with valuable risk information. Within this context, this work is the first study producing corporate yield spreads for all S&P-rated bonds of G20 nations to explain their comparative riskiness. The option-adjusted spread analysis is an advanced method that enables us to compare the bonds with embedded options and different cash flow characteristics. For securities with embedded options, the volatility in the interest rates plays a role in ascertaining whether the option is going to be invoked or not. Therefore, researchers need a spread that, when added to all the forward rates on the tree, will make the theoretical value equal to the market price. The spread that satisfies this condition is called the option-adjusted spread, since it considers the option embedded into the issue. Ultimately, this work investigates the credit risk differentials of S&P rated outstanding bonds issued by the G20 nations to provide international finance professionals with option-adjusted corporate yield spreads showing the credit risk attributable to debt instruments. Detailed results computed using OAS methodology are presented in tables and used to answer the six vital credit-risk-related questions introduced in the introduction.

Corporate bond spreads and understated pension liabilities

PurposeThe question is whether debt market investors see through managers' attempts to hide their pension obligations. The authors establish a robust relation between understated pension liabilities and corporate bond yield spreads after controlling for factors that have been previously identified as having a significant impact on firms' cost of borrowing. The results support the idea that bond market investors are not being misled by the use of high pension liability discount rates by some companies to lower their reported pension obligations. For a small fraction of debt issuers, the reported pension liabilities are larger than the pension liabilities valued at the stipulated interest rate benchmarks. For these issuers with overstated pension liabilities, bond investors adjust their borrowing costs downward.Design/methodology/approachThe authors investigate the relation between corporate bond yield spreads and understated pension liabilities relative to long-term Treasury and high-grade corporate bond yields. They aim to answer two questions. First, what are the sizes of over or understated pension liabilities relative to guideline benchmarks? Second, do debt market investors see through the potential management manipulation of pension discount rates? The authors find that firms with large understated pension liabilities face higher marginal borrowing costs after taking into account issue-specific features, firm characteristics, macroeconomic conditions and other pension information such as funded status and mandatory contributions.FindingsThe average understated projected benefit obligations (PBOs) are understated by $394.3 and $335.6, equivalent to 3.5 and 3.0% of the beginning of the fiscal year market value, respectively. The average understated accumulated benefit obligations (ABOs) are understated by $359.3 and $305.3 million, equivalent to 3.1 and 2.6%, of the beginning of the fiscal year market value, respectively. Relative to AA-grade corporate bond yields, the average difference between firm pension discount rates and benchmark yields becomes much smaller; the percentage of firm pension discount rates higher than benchmark yields is also much smaller. As a result, understated pension liabilities become negligible. The authors establish a robust relation between corporate bond yield spreads and measures of understated pension liabilities after controlling for issue-specific features, firm characteristics, other pension information (funded status and mandatory contributions), macroeconomic conditions, calendar effects and industry effects.Originality/valueS&P Rating Services recognizes the issue that there is considerably more variability in discount rate assumptions among companies than in workforce demographics or the interest rate environment in which firms operate (Standard and Poor's, 2006). S&P also indicates that it would be desirable to normalize different discount rate assumptions but acknowledges that it is difficult to do so. In practice, S&P Rating Services conducts periodic surveys to see whether firms' assumed discount rates conform to the normal standard. The paper makes an initial attempt to quantify the size of understated pension liabilities and their impact on corporate bond yield spreads. This approach can be extended to study firms' costs of equity capital, the pricing of seasoned equity offerings and the pricing of merger and acquisition transaction deals, among other questions.

Spillover Effects from the Volkswagen Emissions Scandal: An Analysis of Stock and Corporate Bond Markets

This study examines spillover effects following Volkswagen’s admission of emissions cheating. We first estimate initial operational losses of 8.45% of Volkswagen’s equity market capitalization on the date before the announcement, reputational losses up to five times these losses, and significant negative shocks to its stocks and bonds. Analyzing spillover effects from this shock beyond the usually only measured losses in equity value, we find significant negative net spillover effects to European competitors and suppliers in both stock and bond markets. Studying the economic effects in more detail, we show that Volkswagen’s total losses of 27.4 billion euros in terms of changes in equity market values over the first five event days are almost entirely composed of abnormal losses. Furthermore, competitors (suppliers) overall suffered 18.3 (12.6) billion euros of abnormal losses during this time, with 60% (69%) of the firms exhibiting negative changes, especially European competitors and suppliers connected to Volkswagen. These figures are further increased by negative bond market value changes. Overall, our results strongly emphasize that neglecting debt holders losses can lead to an underestimation of such events.

Multi-Class Imbalanced Corporate Bond Default Risk Prediction Based on the OVO-SMOTE-Adaboost Ensemble Model

Corporate bond default risk prediction is important for regulators, issuers and investors in the bond market. We propose a new approach for multi-class imbalanced corporate bond risk prediction based on the OVO-SMOTE-Adaboost ensemble model, which integrates the one-versus one (OVO) decomposition method, the synthetic minority over-sampling technique (SMOTE) and the Adaboost ensemble method. We categorize corporate bond default risk into three classes: very low default risk, relatively low default risk and high default risk, which is more scientific than the traditional two-class bond default risk, and carry out empirical experiments by respectively using DT, SVM, Logit and MDA as basic classifiers. Empirical results show that the prediction performance of the OVO-SMOTE-Adaboost (DT) model is overall better than the other three ensemble models such as OVO-SMOTE-Adaboost (SVM), OVO-SMOTE-Adaboost (Logit) and OVO-SMOTE-Adaboost (MDA). In addition, the OVO-SMOTE-Adaboost (DT) model greatly outperforms the OVO-SMOTE (DT) model, which is a single classifier model based on OVO and SMOTE without Adaboost. Therefore, the OVO-SMOTE-Adaboost (DT) model has satisfying performance of multi-class imbalanced corporate bond default risk prediction and is of great practical significance.

Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.

Can the dual-rating regulation improve the rating quality of Chinese corporate bonds?

We developed a dual-reputational rating shopping model to introduce public and institutional reputations. Investor’s and regulator’s penalty rates are described as public and institutional reputations, respectively. We achieved the available conditions of single-rating and dual-rating regulations to prevent rating inflation in this model. To examine the regulatory effects of different types of regulations on Chinese corporate bond ratings, we utilize panel ordered logit models. Theoretical analysis and empirical tests show that, when the reputation effect is low, the single-rating regulation is better at improving rating quality, and when the reputation effect is high, the dual-rating regulation induces rating agencies to provide more accurate ratings. Compared to the regulatory effects of the single-rating and the multi-rating regulations, the dual-rating regulation most effectively improves the rating quality of corporate bonds and prevents rating inflation.

Announcements Effect of Corporate Bond Issuance on Stock Returns: Evidence from Chile

This study measures the announcement effect of corporate bond issuance on stock returns for companies listed on the Santiago de Chile Stock Exchange (BCS). The sample is made up of 29 firms and 87 corporate bond issuance announcements during the 2010-2017 period. The announcement effect of corporate bond issuance on stock return is measured by an event study. This methodology allows to calculate abnormal returns for the days of the event period. The results show that the average abnormal return on the day of the announcement is negative (between -0.09% and -0.03%), but it is not statistically significant. However, the average abnormal return on the day after the announcement is positive (between 0.27% and 0.32%) and has statistical significance. The significant and positive average abnormal return on the day after the announcement suggests a late market reaction. The study shows that there is a significant signaling effect of bond issuance announcements on stock returns.