Search

This paper investigates whether the COVID-19 cases and death rates affect the dynamic correlation of corporate-government bond yields. Therefore, this study uses the daily corporate bond data with different ratings of bonds along with the COVID-19 data at both the US and global levels. Using the quantile regression approach, it produces the following results. First, the impact of daily cases differs from that of death rates both locally and globally. Second, the impact of local cases and death cases on the government-AAA yields correlation at a given quantile tends to reverse when the BBB bonds are used in the analysis. Third, global death rates significantly affect the correlation series the most at the higher quantiles. Lastly, AAA-rated bonds show higher sensitivity to COVID-19 cases and death rates than BBB-rated bonds. This finding indicates that relatively high-quality bonds are more susceptible to the pandemic period and thus calls for careful evaluation of assets included in investors’ portfolios. This study assumes that local COVID-19 data provide a better implication for constructing bond portfolios than global data. That is, their economic impact depends on the rating of the bond and tends to vary more across correlation quantiles.

Viewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.

The objective of investors to invest their money was to maximize return, although they were subject to constraints, primarily risk, so this study aims to provide an alternative to the formation of portfolios and corporate bonds to obtain optimal returns with acceptable risk. The alternative model is to combine Graham's stock selection model with the formation of an optimal portfolio model, namely the Markowitz model. The results of the selection using the Graham model of defensive investors and aggressive investors are 9 and 13 stocks, respectively, of the 45 stocks listed on the LQ45 index. From the selected stocks, the optimal portfolio is formed using the Markowitz model. The results show that the Markowitz model optimizing portfolio provides better performance (reward to variability ratio) than the LQ45 portfolio index with a yield difference of 13.68 – 20.24% per year. Furthermore, the selection of corporate bonds for the optimal portfolio resulted in 8 corporate bonds with a portion of each bond, and this portfolio can generate rates of 8.88% per annum. JEL: G10, G11, G12

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine the predictions from 20 candidates of linear, nonlinear and rule-based algorithms, and we exploit a data set of predictors including security-specific factors, macro-financial indicators and measures of economic uncertainty. We find that the most promising approach consists of model combinations trained on security-specific characteristics and a limited number of well-identified, theoretically sound recovery rate determinants, including uncertainty measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in designing micro- and macro-prudential policies. [ABSTRACT FROM AUTHOR]

The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. The effectiveness of the financial market for this transition to a low-carbon economy depends on attracting investors and removing financial market roadblocks. This paper investigates the differential bond performance of green vs non-green bonds with (1) a dynamic portfolio model that integrates negative as well as positive externality effects and via (2) econometric analyses of aggregate green bond and corporate energy time-series indices; as well as a cross-sectional set of individual bonds issued between 1 January 2017, and 1 October 2020. The asset pricing model demonstrates that, in the long-run, the positive externalities of green bonds benefit the economy through positive social returns. We use a deterministic and a stochastic version of the dynamic portfolio approach to obtain model-driven results and evaluate those through our empirical evidence using harmonic estimations. The econometric analysis of this study focuses on volatility and the risk–return performance (Sharpe ratio) of green and non-green bonds, and extends recent econometric studies that focused on yield differentials of green and non-green bonds. A modified Sharpe ratio analysis, cross-sectional methods, harmonic estimations, bond pairing estimations, as well as regression tree methodology, indicate that green bonds tend to show lower volatility and deliver superior Sharpe ratios (while the evidence for green premia is mixed). As a result, green bond investment can protect investors and portfolios from oil price and business cycle fluctuations, and stabilize portfolio returns and volatility. Policymakers are encouraged to make use of the financial benefits of green instruments and increase the financial flows towards sustainable economic activities to accelerate a low-carbon transition. [ABSTRACT FROM AUTHOR]