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The study’s pertinence lies in its focus on the performance of green bond markets in addressing environmental concerns. The purpose of this study is to ascertain if current issuances of high credit ratings by credit rating agencies can be considered safe by investors, through the consideration of the margin of safety, amidst the prevailing high credit growth.  A quantitative research methodology determining the margin of safety of issuers, using operating incomes and interest expense, derived from annual financial statements of 30 randomly selected companies, which are considered adequate for inferential purposes, from the Climate Bond Initiative database is undertaken. The decline in EBIT before the balance after interest expense is paid becomes zero is ascertained in conjunction with the allocated credit rating.  A sampled mean of a margin of safety of 58.79 % leads to an estimated population mean results of a margin of safety above or equal 48% and below or equal to 68.41%, at 95% confidence level and a standard deviation of 27.41%. The corresponding investment credit ratings reflect a 77% in relative frequency of which 3.3% is assigned the highest AAA rating. The results demonstrate that it takes close to a minimum of 50% and a maximum of 68% deterioration in operating income before balances after interest expense are wiped out. Hence the study concludes that the prevailing credit ratings being issued by credit rating agencies can be trusted by green bond investors based on the substantive margin of safety figures of issuers.

We study the sinking fund provision in 1,533 nonconvertible corporate bonds issued from 1980 to 1996 with the year 1996 being the last year of inclusion for these provisions in fixed coupon non-convertible corporate bonds. The period 1980 to 1996 is classified as the period of high interest rates (Tewari, Byrd, and Ramanlal, 2015). We find that the provision addresses agency issues due to reinvestment risk, credit improvement, and growth options. When the interest rates are high, investment grade issues with sinking fund have longer maturities (> 20 years) by a ratio exceeding 4:1, while non-sinking fund issues have shorter maturities (≤ 20 years) by a ratio of about 2:1. High yield issues with sinking fund experience credit risk improvement, while non-sinking fund issues experience credit risk deterioration. When the rates are low and expected to rise, the provision is likely to present accumulation threat to the firm thereby, deterrent to its use by the firms. Also, firms use the sinking fund as a call option to capture the benefits of growth. These results suggest that the sinking fund provision has the potential to make a comeback in the corporate debt in a high-interest rate environment and when the rates are likely to fall. The current environment of persistent high rates presents conditions ripe for inclusion of the sinking fund provision in the corporate bonds.

The aim of this paper is to calibrate the Longstaff, Mithal and Neis (2005) model (LMN model) based on the J-law (see Jerbi (2015) to implement the credit risk in the economic scenarios generator. The LMN model, based on the assumptions of the normality of the risks is used to establish a link between the spread, the probability of default and the liquidity risk. The related assumptions seem unrealistic given the depth of the distribution that characterizes the financial and economic data. In this context, we develop an LMN model based on the J-law taking into account the effects of Skewness and Kurtosis related to the two series of the CDS premium and the corporate bond prices. We use a simulated data that are from January 2009 to December 2016. We estimate the model parameters. The LMN model based on the J-law fit better with the reality than the one based on the normal Law.      

Using a large sample of bond rating downgrades of US firms announced by the three largest rating agencies during 1997-2023, I find that cumulative abnormal stock returns (CAR) spanning the day of and day following downgrade announcements are significantly and negatively related to annual report readability, indicating that downgrades are more informative among firms with more readable annual reports. My baseline regression results indicate that a one standard deviation improvement in annual report readability corresponds to a reduction in the announcement CAR of approximately forty-three basis points, which is economically meaningful given that it constitutes roughly 27% of the sample average announcement CAR of -1.62%. Furthermore, additional analysis reveals that the informativeness enhancing effect that better annual report readability has on rating downgrades is stronger among firms that should be more difficult to monitor and evaluate, including lower rated firms, firms with high stock return volatility, and younger firms. Overall, the results strongly support the hypothesis that well-written annual reports facilitate more timely and informative bond rating downgrades, especially among more informationally problematic firms.

This study aims to empirically examine the effect of macroeconomic variables on credit risk in each business sector in Indonesia. Using time-series quarterly data during the period 2011q1-2019q2, this study utilized the Autoregressive Distributed Lag (ARDL) model. The results of this study explain that macroeconomic variables namely GDP growth in the long run have a significant negative effect on credit risk in 6 sectors and in the short term have a significant negative effect on 6 sectors. Inflation has a significant positive effect in the long run on one sector, namely the provision of accommodation and provision of food and drink, and a significant negative effect on 6 sectors, in the short term inflation has a significant positive effect on 7 sectors and a significant negative effect on one sector, namely education services. The last variable is the long-term loan interest rate which has a positive effect on 7 sectors and in the short term has a significant positive effect on 6 sectors on the high value of credit risk in each business sector in Indonesia. The result indicating that macroeconomic variables have a real impact on credit risk.