The development of green bond markets is important for advancing energy efficiency, supporting renewable energy, encouraging sustainable investments, and safeguarding the environment. However, the inherent complexity and uncertainty of these markets pose significant challenges for both investors and researchers. In this study, we focus on analyzing the S&P Green Bond Index, a leading benchmark for monitoring the global green bond market. We introduce a new high-dimensional statistical method, the Quantile Group Adaptive Lasso, designed to accurately predict the returns of this index. Our empirical results demonstrate that this model surpasses several established forecasting techniques in both accuracy and stability. Furthermore, our analysis of economic significance highlights the critical influence of traditional energy-related predictors from G7 and BRICS countries on the global green bond markets. We also find that monetary policies and macroeconomic factors, such as M2 money supply, CPI, and government bond yields, play vital roles. Additionally, the robustness of our proposed method is confirmed. Overall, our study provides a powerful tool that not only significantly enhances forecasting performance but also deepens the understanding of the interplay between trends in green bond markets and information from energy sectors and broader economic conditions.

中文翻译：

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分析绿色债券指数：一种新颖的基于分位数的高维方法


绿色债券市场的发展对于提高能源效率、支持可再生能源、鼓励可持续投资和保护环境非常重要。然而，这些市场固有的复杂性和不确定性给投资者和研究人员都带来了重大挑战。在这项研究中，我们重点分析了标准普尔绿色债券指数，该指数是监测全球绿色债券市场的领先基准。我们引入了一种新的高维统计方法，即 Quantile Group Adaptive Lasso，旨在准确预测该指数的回报。我们的实证结果表明，该模型在准确性和稳定性方面都超过了几种已建立的预测技术。此外，我们对经济意义的分析强调了 G7 和金砖国家的传统能源相关预测因子对全球绿色债券市场的关键影响。我们还发现，货币政策和宏观经济因素，如 M2 货币供应量、CPI 和政府债券收益率，起着至关重要的作用。此外，我们提出的方法的稳健性也得到了证实。总体而言，我们的研究提供了一个强大的工具，不仅显著提高了预测性能，还加深了对绿色债券市场趋势与能源行业和更广泛经济状况信息之间相互作用的理解。