A memory in the bond: Green bond and sectoral investment interdependence in a fractionally cointegrated VAR framework

Energy Economics(2023)

引用 2|浏览0
暂无评分
摘要
The urgency surrounding environmental sustainability has triggered an innovation of financing channels for climate and environmental projects. Green bond as one such channel has garnered immense interest from investors, with an implicit view that this fixed-income instrument is a relatively safer choice as an investment portfolio. Yet, the uncomfortable spread of greenwashing as a marketing spin has subjected green bonds to significant market volatility, at least as much as other financial assets or sectoral indices if not more. Whether green bonds as a financial instrument may incur losses to the extent of the loss in various sector indices, can be gauged by studying the nature of their contemporaneous growth. In this paper, we use daily data on green bonds and several S&P sectoral indices and a fractionally cointegrated vector autoregression framework (FCVAR) to study the extent to which green bonds dynamically co-move with various sectoral indices. Such a co-movement, if any, would elicit the extent to which a variation of uncertainty would determine an investor’s inclination to the diversification of a portfolio between an investment in a sectoral index and a green bond. The identifying mechanism is the shock-dissipation speed, which also informs a policymaker before choosing the right instrument to stabilise the system. We show that the system-wide shocks indeed dissipate slower than could be predicted by a conventional cointegrated VAR system. Further, the property of the slow error correction within the dynamic system of Green Bond and sectoral S&P indices, for instance, may demonstrate the speed of adjustment of the global economy to sudden shocks. Rigorous predictions exercises complement our baseline conclusions.
更多
查看译文
关键词
Green bond,S&P sectoral performance,Long memory error corrections,Fractionally cointegrated VAR
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要