Dynamic Interlinkages between the Twitter Uncertainty Index and the Green Bond Market: Evidence from the Covid-19 Pandemic and the Russian-Ukrainian Conflict

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-06-26 DOI:10.1007/s10614-024-10666-6
Onur Polat, Berna Doğan Başar, İbrahim Halil Ekşi
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Abstract

This study examines the time-varying connectedness between green bonds, Twitter-based uncertainty indices, and the S&P 500 Composite Index. We implement the time- and frequency-based connectedness methodologies and employ data between April 1, 2014 and April 21, 2023. Our findings suggest that (i) connectedness indices robustly capture prominent incidents during the episode; (ii) Twitter-based uncertainty indices are the highest transmitters of return shocks; (iii) net return spillovers transmitted by the S&P 500 Index sharply increased in 2020:1–2020:3, stemmed by the stock market crash in February 2020; and (iv) Twitter-based uncertainty indices showed significant net spillovers in July and November 2021.

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推特不确定性指数与绿色债券市场之间的动态相互联系:科维德-19 大流行病和俄乌冲突的证据
本研究探讨了绿色债券、基于 Twitter 的不确定性指数和 S&P 500 综合指数之间随时间变化的关联性。我们采用了基于时间和频率的关联性方法,并使用了 2014 年 4 月 1 日至 2023 年 4 月 21 日期间的数据。我们的研究结果表明:(i) 连接度指数能够稳健地捕捉到事件期间的突出事件;(ii) 基于 Twitter 的不确定性指数是回报冲击的最大传播者;(iii) S&P 500 指数传播的净回报溢出效应在 2020:1-2020:3 期间急剧增加,2020 年 2 月的股市暴跌是其主要原因;(iv) 基于 Twitter 的不确定性指数在 2021 年 7 月和 11 月显示出显著的净溢出效应。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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