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Today, despite the literature abounds with mixed results and no consensus has been indeed reached, it seems anyway that a larger proportion of researchers provides evidence in favor of a green bond premium. In other words, green bonds’ yields would be lower than that of conventional bonds with the same characteristics, which makes these green bonds priced higher from an investor perspective, or funded cheaper from an issuer perspective. In addition to that, the spread appears to vary depending on the sector, credit-ratings or issuer type. 
Issuers’ lower borrowing costs through the debt market may constitute a first potential answer to the research question ‘How and why are green bonds arousing more interest nowadays?’, however it is certainly not the only explanation to their surge. Indeed, some authors highlight the reporting, labeling and monitoring costs GBs issuers have to bear, saying that the premium they obtain is offset by these costs. Therefore, what is the point to issue green bonds? Is there any interest other than the premium that motivates issuers to go for GBs? This paper investigates the case of listed corporations and, more specifically, through a difference-in-differences methodology and using historical stock returns data, we first investigate the effect of first green bond issue on the systematic risk as well as on the abnormal returns. In a second time, we investigate the stock market reaction to first green bond issue announcement. 
We find that neither the systematic risk nor the abnormal returns are affected by first GB issue. Similarly, the stock market seems insensitive to first green bond issue announcement since both the average abnormal returns and cumulative average abnormal returns appear not statistically different from zero. Note, however, that tests were conducted on a sample comprised of 2 x 33 large capitalization (mainly) stocks, which is relatively small and not diversified in terms of size; the problems that come along therefore constitute the main drawback of the study.

Green bonds --- Systematic Risk --- Abnormal returns --- Event-Study --- Sciences économiques & de gestion > Finance

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Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Coins, banknotes, medals, seals (numismatics) --- Index parameter --- estimation --- wrapped stable --- Hill estimator --- characteristic function-based estimator --- asymptotic --- efficiency --- GARCH model --- HARCH model --- PHARCH model --- Griddy-Gibs --- Euro-Dollar --- safe-haven assets --- gold price --- Swiss Franc exchange rate --- oil price --- generalized Birnbaum–Saunders distributions --- ACD models --- Box-Cox transformation --- high-frequency financial data --- goodness-of-fit --- banking competition --- credit risk --- NPLs --- Theil index --- convergence analysis --- interest rates --- yeld curve --- no-arbitrage --- bonds --- B-splines --- time series --- multifractal processes --- fractal scaling --- heavy tails --- long range dependence --- financial models --- Bitcoin --- capital asset pricing model --- estimation of systematic risk --- tests of mean-variance efficiency --- t-distribution --- generalized method of moments --- multifactor asset pricing model --- Lerner index --- stochastic frontiers --- shrinkage estimator --- seemingly unrelated regression model --- multicollinearity --- ridge regression --- financial incentives --- public service motivation --- job performance --- job satisfaction --- intention to leave

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Coins, banknotes, medals, seals (numismatics) --- Index parameter --- estimation --- wrapped stable --- Hill estimator --- characteristic function-based estimator --- asymptotic --- efficiency --- GARCH model --- HARCH model --- PHARCH model --- Griddy-Gibs --- Euro-Dollar --- safe-haven assets --- gold price --- Swiss Franc exchange rate --- oil price --- generalized Birnbaum–Saunders distributions --- ACD models --- Box-Cox transformation --- high-frequency financial data --- goodness-of-fit --- banking competition --- credit risk --- NPLs --- Theil index --- convergence analysis --- interest rates --- yeld curve --- no-arbitrage --- bonds --- B-splines --- time series --- multifractal processes --- fractal scaling --- heavy tails --- long range dependence --- financial models --- Bitcoin --- capital asset pricing model --- estimation of systematic risk --- tests of mean-variance efficiency --- t-distribution --- generalized method of moments --- multifactor asset pricing model --- Lerner index --- stochastic frontiers --- shrinkage estimator --- seemingly unrelated regression model --- multicollinearity --- ridge regression --- financial incentives --- public service motivation --- job performance --- job satisfaction --- intention to leave