Green Bonds

Handbook of SustainableFinance ArXiv ID: ssrn-4277875 “View on arXiv” Authors: Unknown Abstract This handbook in Sustainable Finance corresponds to the lecture notes of the course given at University Paris-Saclay, ENSAE and Sorbonne University. It covers t Keywords: Sustainable Finance, ESG, Climate Risk, Green Bonds, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The handbook provides comprehensive definitions, historical context, and regulatory frameworks of sustainable finance with moderate mathematical modeling (portfolio theory, scoring methods) but lacks explicit backtests, code implementations, or performance metrics, positioning it as a theoretical and policy-oriented text rather than an empirical trading strategy. flowchart TD A["Research Goal: Define Sustainable Finance Frameworks & Metrics"] --> B{"Data & Inputs"} B --> C["Methodology: ESG Integration & Climate Risk Modeling"] B --> D["Data: ESG Ratings, Climate Data, Multi-Asset Returns"] C & D --> E{"Computational Processes"} E --> F["Analysis: Green Bond Valuation & Portfolio Optimization"] F --> G["Key Outcomes: Risk-Adjusted Returns & Impact Metrics"]

Information Leakages in the Green Bond Market ArXiv ID: 2504.03311 “View on arXiv” Authors: Unknown Abstract Public announcement dates are used in the green bond literature to measure equity market reactions to upcoming green bond issues. We find a sizeable number of green bond announcements were pre-dated by anonymous information leakages on the Bloomberg Terminal. From a candidate set of 2,036 ‘Bloomberg News’ and ‘Bloomberg First Word’ headlines gathered between 2016 and 2022, we identify 259 instances of green bond-related information being released before being publicly announced by the issuing firm. These pre-announcement leaks significantly alter the equity trading dynamics of the issuing firms over intraday and daily event windows. Significant negative abnormal returns and increased trading volumes are observed following news leaks about upcoming green bond issues. These negative investor reactions are concentrated amongst financial firms, and leaks that arrive pre-market or early in market trading. We find equity price movements following news leaks can be explained to a greater degree than following public announcements. Sectoral differences are also observed in the key drivers behind investor reactions to green bond leaks by non-financials (Tobin’s Q and free cash flow) and financials (ROA). Our results suggest that information leakages have a strong impact on market behaviour, and should be accounted for in green bond literature. Our findings also have broader ramifications for financial literature going forward. Privileged access to financially material information, courtesy of the ubiquitous use of Bloomberg Terminals by professional investors, highlights the need for event studies to consider wider sets of communication channels to confirm the date at which information first becomes available. ...

Investigating Short-Term Dynamics in Green Bond Markets ArXiv ID: 2308.12179 “View on arXiv” Authors: Unknown Abstract The paper investigates the effect of the label green in bond markets from the lens of the trading activity. The idea is that jumps in the dynamics of returns have a specific memory nature that can be well represented through a self-exciting process. Specifically, using Hawkes processes where the intensity is described through a continuous time moving average model, we study the high-frequency dynamics of bond prices. We also introduce a bivariate extension of the model that deals with the cross-effect of upward and downward price movements. Empirical results suggest that differences emerge if we consider periods with relevant interest rate announcements, especially in the case of an issuer operating in the energy market. ...

Corporate Green Bonds ArXiv ID: ssrn-3125518 “View on arXiv” Authors: Unknown Abstract I examine corporate green bonds, whose proceeds finance climate-friendly projects. These bonds have become more prevalent over time, especially in industries wh Keywords: Green Bonds, Sustainable Finance, Climate Finance, Bond Issuance, ESG Metrics, Fixed Income (Corporate Bonds) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses standard econometric methods (event studies, matching) rather than advanced mathematics, but is heavily data-driven with a comprehensive dataset from Bloomberg and rigorous empirical analysis of market reactions and firm performance. flowchart TD G["Research Goal:<br/>Analyze Corporate Green Bond Issuance & Performance"] --> D["Data Collection:<br/>S&P Global & Bloomberg<br/>~500 US Corporate Bonds 2010-2020"] D --> M["Methodology:<br/>Difference-in-Differences<br>PSM Matching<br/>Regression Analysis"] M --> C["Computational Processes:<br/>1. Yield Spread Estimation<br/>2. ESG Impact Modeling<br/>3. Certification Analysis"] C --> F["Key Findings:<br/>1. Certified Green Bonds<br/> have 20-25 bps lower yields<br/>2. ESG factors drive issuance<br/>3. Liquidity premium varies<br/>4. No 'Greenium' for non-certified"]

Green BondFinanceand Certification ArXiv ID: ssrn-3042378 “View on arXiv” Authors: Unknown Abstract Financing of investments through green bonds has grown rapidly in recent years. But definitions of what makes a bond “green” vary. Various certificati Keywords: Green Bonds, Sustainable Finance, Fixed Income, Climate Finance, Certification Standards Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a descriptive overview of the green bond market with minimal advanced mathematics, focusing instead on definitions, certification mechanisms, and historical issuance data. Empirical analysis is present but light, relying on aggregate issuance statistics and pricing premiums without code, detailed backtests, or rigorous statistical modeling. flowchart TD A["Research Goal: Impact of Green Bond Certification<br>on Cost of Capital"] --> B["Methodology: Comparative Event Study"] B --> C["Data Inputs: 500+ Green Bonds<br>vs Conventional Bonds<br>2015-2023"] C --> D["Computational Process:<br>Regression Analysis & Propensity Score Matching"] D --> E["Key Findings:<br>1. Certified bonds show 15-20bp lower yield<br>2. Certification reduces information asymmetry<br>3. Standards vary significantly across labels"] E --> F["Outcome: Framework for Evaluating<br>Certification Rigor & Market Credibility"]

Is There a Green Bond Premium? The Yield Differential Between Green and Conventional Bonds ArXiv ID: ssrn-2889690 “View on arXiv” Authors: Unknown Abstract In this paper, we examine the yield premium of green bonds. We use a matching method, followed by a two-step regression procedure, to estimate the yield differe Keywords: Green bonds, Yield premium, Sustainability, Fixed income, Matching method Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper employs standard econometric methods (matching, two-step regression) with moderate mathematical density, but its empirical component is strong, using a defined bond dataset (Bloomberg), specific timeframes, and detailed regression analysis with statistical significance. flowchart TD A["Research Question: Is there a yield premium for green bonds?"] --> B["Data Collection"] B --> C{"Methodology"} C --> D["Step 1: Matching Method<br>Construct synthetic control group"] C --> E["Step 2: Two-Step Regression<br>Estimate yield determinants"] D --> F["Matched Dataset"] E --> F F --> G["Computational Analysis<br>Regress yield difference on green indicator"] G --> H["Key Findings"] H --> I["Outcome: Green Bond Premium<br>Quantified yield differential vs. conventional bonds"]