Credit Risk Volatility: Evidences From the Green Bond Market

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

Institutional structure formation of the global green finance market

The article studies the strategic directions of the Eurasian Development Bank as an important element of the institutional structure of the global green finance market.The current differentiated global structure of green finance market institutions has been shown, and the substantive focus of organisations at two levels of the institutional structure of this market has been shown. A statistical overview of green finance institutions by country, region, type and financial instruments used has been made. The role of multilateral development banks in the structure of such institutions has been shown. The need for the formation of institutions in the green financial market segment as a necessary element of green finance has been substantiated.The structure of institutions on two levels has been given: 1) subjects – participants of the green bond market; 2) a set of institutions developing and shaping the methodology for green financial instruments assessment, standards, taxonomy, ratings. Using the global green bond market as an example, up-to-date statistics and analysis of the broader composition of issuers of green financial instruments by country, world region, sovereign and corporate participants, and development institutions have been presented. Emphasis has been placed on public issuers and the participation of multilateral development institutions in financing green economy projects in various countries. The experience of Eurasian Economic Union countries in developing green finance has been summarised and the prospects for Russia and Kazakhstan in the green finance movement have been noted.The institutions of the differentiated structure of the global green finance market identified in the study have been grouped into two levels depending on the profile of their participation in the green economy; the directions and tools for the prospective development of the Eurasian Development Bank’s green finance activities have been defined; recommendations for prospective aspects of the methodological and analytical activities of the bank in the context of the Eurasian Development Bank Strategy 2022–2026 have been proposed.The study applied an analysis of scientific literature in the field of institutional theory, green economy and green finance, statistical, comparative, factual analysis, review and analysis of information from official websites of international development institutions, government and corporate entities, international rating agencies included in the architecture of the global green finance market.

The Influence of ASEAN Bond Market Integration in the Philippines’ Inflationary Pressures, Credit Expansions, and its Challenges

Bond markets have grown mature in many countries; however, the quality of financial integration varies across ASEAN economies. In the case of bond markets in the ASEAN +3, they experienced fast development; however, they are still less integrated. This study attempts to examine the ramifications of the ASEAN bond market integration and past crises to the Philippines’ inflation, credit, and growth and identify what impedes the development of the bond market for the period of 1992 to 2017. The study also aims to have a more in-depth analysis on preventing rises from happening and controlling both credit expansions and inflationary pressures. The Ordinary Least Square method (OLS) was used to examine the relationship of inflation, credit, bond market index, real interest rate, and integration to the Philippines’ growth. This led to this paper providing empirical insights that credit has a significant positive relationship with GDP growth; while, inflation has a significant negative relationship with GDP growth. However, the bond market index and integration showed insignificant negative results. This study provides possible reasons for the said conclusion and suggests ways not only to develop and grow the debt market in the Philippines but also to sustain long-run economic stability and growth to become on par with other ASEAN economies.

Long-term Financing: Exploring the Recent Advances in the Brazilian Bond Market

ABSTRACT Context: in Brazil, there was an expansion of private funding via bond issuances, especially since 2017. Before that period, the sources of long-term financing were concentrated on public funding. Objective: this study aims to explore the main factors that could have positively affect Brazilian bond market and if it would be possible to improve project financing through this debt instruments. Methods: using mixed methods with econometric tests and qualitative interview analysis, this study assesses which were the factors that supported this growth and if there is any difference across industries. Results: we found that a change in the market trend has indeed happened around 2017, and it was more pronounced in specific industries such as electricity. Interviewees suggested that increases in demand (possibly triggered by the reduction of public sources of funding and the fall in local interest rates) could be the main factors that supported this change in trend. Conclusions: therefore, this study reinforces the importance of local market conditions and government policies affecting the relative attractiveness of private versus public sources of corporate investment.

VALUE AT RISK (VAR) METODE DELTA-NORMAL BERDASARKAN DURASI UNTUK UKURAN RISIKO OBLIGASI PEMERINTAH

A bond is one of invesment instrument that is basically a debt instrument. In investing, beside getting profit there is also the risk of loss. The risk of loss is unavoidable but it can be manageable. The concept of a portfolio in investing is to minimize risk. Value at Risk (VaR) is a method used to measure risk where VaR states the estimated amount of the maximum loss that will be obtained at a certain level of confidence during a certain period in normal market conditions. In this article the risk of bonds FR0053, FR0056, FR0059, FR0061 and portfolio combinations calculated with VaR value of the Delta-Normal method are calculated based on the duration of the bonds. Normality test of the bond market price return is required before calculating VaR. The results obtained if it is assumed that the bonds are purchased at a price of 100 and with a confidence level of 95%, then the portfolio that has the smallest risk is the Bond portfolio of FR0059 and FR0061 with a VaR value Rp 21,436 (Trillions).

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.