乌克兰语
2019年冠状病毒病(COVID-19)
2019-20冠状病毒爆发
俄罗斯联邦
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
金融危机
金融体系
业务
经济
经济政策
病毒学
凯恩斯经济学
医学
内科学
哲学
语言学
疾病
爆发
传染病(医学专业)
作者
Souhir Amri Amamou,Mouna Ben Daoud,Saoussen Aguir Bargaoui
出处
期刊:Journal of Economic Studies
[Emerald (MCB UP)]
日期:2024-06-04
标识
DOI:10.1108/jes-01-2024-0061
摘要
Purpose Without 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/approach In 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. Findings Authors 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/value This 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.
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