Green bonds forecasting: evidence from pre-crisis, Covid-19 and Russian–Ukrainian crisis frameworks

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)]
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Rainy完成签到,获得积分20
1秒前
小马甲应助激昂的君浩采纳,获得10
1秒前
Billy发布了新的文献求助10
5秒前
5秒前
研友_nVqwxL发布了新的文献求助10
6秒前
6秒前
库昊的假粉丝应助暴发户采纳,获得30
7秒前
SciGPT应助严西采纳,获得10
7秒前
9秒前
Celine完成签到,获得积分10
9秒前
莎莎发布了新的文献求助30
11秒前
老王发布了新的文献求助10
12秒前
13秒前
14秒前
坚强的严青完成签到,获得积分10
14秒前
氢气完成签到,获得积分10
15秒前
17秒前
17秒前
18秒前
激昂的君浩完成签到,获得积分10
19秒前
科研小白应助mylpp采纳,获得10
21秒前
21秒前
LYD完成签到,获得积分10
21秒前
21秒前
自然乘云发布了新的文献求助10
21秒前
22秒前
严惜完成签到,获得积分10
23秒前
myn1990发布了新的文献求助10
23秒前
愤怒的之玉完成签到 ,获得积分10
24秒前
神勇的天菱完成签到,获得积分10
25秒前
DD发布了新的文献求助10
25秒前
静途发布了新的文献求助10
26秒前
Jasper应助十一采纳,获得10
27秒前
自然乘云完成签到,获得积分20
28秒前
111完成签到,获得积分10
28秒前
晚意意意意意完成签到 ,获得积分10
30秒前
核桃小丸子完成签到 ,获得积分10
30秒前
莎莎完成签到,获得积分10
30秒前
31秒前
高分求助中
Licensing Deals in Pharmaceuticals 2019-2024 3000
Effect of reactor temperature on FCC yield 2000
Very-high-order BVD Schemes Using β-variable THINC Method 1020
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
Mission to Mao: Us Intelligence and the Chinese Communists in World War II 600
The Conscience of the Party: Hu Yaobang, China’s Communist Reformer 600
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3299776
求助须知:如何正确求助?哪些是违规求助? 2934644
关于积分的说明 8470036
捐赠科研通 2608208
什么是DOI,文献DOI怎么找? 1424075
科研通“疑难数据库(出版商)”最低求助积分说明 661827
邀请新用户注册赠送积分活动 645574