Adaptive LASSO-MGARCH for Multivariate Volatility Forecasting

There has been an increased interest in literature to examine the risk and returns between green and conventional bonds during the last decade. However, the existing literature is silent regarding investigating green bonds and their reactions to regional and global shocks. We attempt to close this gap by gathering green and conventional bonds data issued by the same firms. Using data from 262 firms that issue both sets of bonds and trade in the same market, we are able to control/eliminate for firm-specific factors that can impact the bond spreads. Upon introducing US and EU macroeconomic announcements and economic uncertainty, we find that the green bonds are more resilient from specific shocks, when compared to conventional bonds. We further expand our study to systematically assess the impact of the Covid-19 pandemic on both bonds. Our comprehensive findings suggest an increase in green bonds’ uptake post-Covid-19 pandemic, and its significant evolvement compared to pre-Covid-19 proves green bonds popularity. Our result shows greater resilience of green bonds from specific Covid-19 shocks and uncertainties.

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability.

PurposeThis paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.Design/methodology/approachThe authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.FindingsFrom the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.Practical implicationsThe quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.Social implicationsThe outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.Originality/valueThis paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

For the Love of the Environment: An Analysis of Green versus Brown Bonds During the COVID-19 Pandemic

PurposeThis paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.Design/methodology/approachGeneric 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.FindingsResults from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.Originality/valueThis study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

We investigate the role of climate risk in the Eurozone sovereign debt market, to evaluate the current pricing of different risk factors in the government spreads of each country. Particular attention is paid to differences between green and non-green bonds, in terms of reactions to climate risk. Weather variables are selected in line with the guidelines of Eurozone climate stress tests, and taken as proxies for acute and chronic physical risk, while EU carbon allowances are included to capture transition risk. Their significance is studied as potential drivers of mean spread variations and for their co-movement with spreads in the case of extreme events, to provide insights for climate risk management and financial policy, with the goal of enhancing the resilience and stability of the financial system. From both analyses, the same results emerge: climate risk is being priced by the market, but differently depending on the country, and green government bonds from different countries have a divergent reaction to climate risk factors. Dutch and French green spreads closely mirror their traditional counterparts, while German, Italian, and Spanish green bonds display lower reactivity. Various explanations are considered, including a “scarcity effect” linking the behavior of green bonds to their abundance relative to total outstanding government debt. Finally, the most relevant risk factors of each country are highlighted, comparing the results to known climate change challenges.

PurposeWithout precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.Design/methodology/approachIn their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.FindingsAuthors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.Originality/valueThis study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

Green bonds: Do investors benefit from third-party certification?

The effects of global climate change and increasing environmental awareness have led to an increase in the significance of climate projects and, accordingly, climate finance and green bonds. Despite the increasing significance, the fact that the price forecasting studies on green bonds are extremely scarce has been the main motivation of this study. The aim of this paper is to forecast the corporate green bond prices with the Artificial Neural Network model and to determine the predictor by addressing the conceptual framework of green bonds. For this purpose, the Multi-Layer Feedback Artificial Neural Network (MLF-ANN) model, in which S&P 500 index prices are determined as input and S&P green bond index prices as output, is designed. To determine whether the conventional bond prices are the predictor of the corporate green bonds, the S&P 500 index was used as the sole input of the forecasting model. The findings show that corporate green bond prices are forecasted with 1.13% Mean Absolute Percentage Error (MAPE) and 98.93% Regression Determination Coefficient (R2). The results of the research provide data to maximize profits and/or minimize risk for green bond investors and market makers, while providing insight into the effectiveness of green bonds in financing climate projects for policy makers. This paper is the first study in the literature in terms of proving the effectiveness of the MLF-ANN model in forecasting corporate green bonds and revealing that conventional bonds are predictor of green bonds. Thus, it is expected that the study will shed light on future studies.

Time-frequency co-movement and network connectedness between green bond and financial asset markets: Evidence from multiscale TVP-VAR analysis

Minimum Connectedness Portfolios and the Market for Green Bonds: Advocating Socially Responsible Investment (SRI) Activity

The Benefits of Issuing Green Bonds: Evidence From China Green Bonds Market

The Creation and Benchmarking of a Green Municipal Bond Index

This study examines the existence of the Green Bond Premium in the U.S. secondary corporate bond market and investigates the impact of a firm’s environmental performance on the green premium. It further analyzes the moderating effects of social and governance performance on the relationship between environmental performance and the green bond premium.The analysis uses a sample of 66 matched pairs of green and conventional corporate bonds, matched through Propensity Score Matching (PSM) to ensure comparability across key bond characteristics. Key variables are obtained from Bloomberg for the period 2019 to 2024.The results confirm the existence of a modest green bond premium, with green bonds trading at slightly lower yields than comparable conventional bonds, after controlling for liquidity differences. However, firm-level environmental performance does not have a statistically significant effect on the green premium. In addition, there is no evidence that social or governance performance moderates the relationship between environmental performance and green premium.

The connectedness of oil shocks, green bonds, sukuks and conventional bonds