Twitter-based economic uncertainties and time-frequency connectedness among cryptocurrencies

IF 1.9 Q2 BUSINESS, FINANCE
Mustafa Kocoglu, Xuan-Hoa Nghiem, Ehsan Nikbakht
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(2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. 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引用次数: 0

Abstract

Purpose

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

Design/methodology/approach

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Findings

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

Practical implications

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Originality/value

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

基于 Twitter 的经济不确定性和加密货币之间的时频关联性
目的 在本研究中,我们旨在调查主要加密货币市场之间的关联性溢出效应。此外,我们还探讨了驱动这种关联性的因素,尤其关注在基于 Twitter 的经济不确定性和美国经济政策不确定性下,加密货币之间的总回报、短期回报和长期回报关联性溢出的情绪性。最后,我们研究了加密货币市场在多大程度上起到了避风港、对冲和分散新闻不确定性的作用。本研究采用了连通性方法,将 Ando 等人(2022 年)的 QVAR 以及 Baruník 和 Krehlík(2018 年)的频率连通性方法结合到 Diebold 和 Yilmaz(2012 年,2014 年)提出的框架中。数据涵盖 2017 年 11 月 10 日至 2023 年 4 月 21 日,并确定和研究了驱动加密货币连通性溢出的因素。我们分析了加密货币间总回报、短期回报和长期回报连通性溢出的情绪性,涉及基于推特的经济不确定性和美国经济政策不确定性。我们应用 Kumar 和 Padakandla(2022 年)开发的小波量化相关性(WQC)方法,探讨了 Twitter 经济不确定性和美国经济政策不确定性对加密货币市场关联性风险溢出的影响。我们的研究结果表明,以太坊和比特币是互联性回报网络中心的净冲击传播者。以太坊和比特币分别拥有加密货币市场最高的市值和价值。这表明,源自这两种加密货币的回报冲击对其他加密货币的影响最大。Tether 和 Monero 是回报冲击的净接收者,而 Cardano 和 XRP 通过回报表现出微弱的冲击传递特性。就回报溢出效应而言,以太坊是最有效的,其次是比特币和恒星币。进一步分析表明,推特经济政策不确定性和美国经济政策不确定性是短期和总体方向性溢出效应的有效驱动因素。这些不确定性指数在短期和总体方向性溢出效应中表现出正的系数符号,而在长期溢出效应中,这些不确定性指数在不同的幅度和频率范围内主要转为负值。此外,我们还发现,随着总关联度指数(TCI)值的增加,市场风险也会上升。同时,我们的实证研究结果还提供了重要证据,证明基于 Twitter 的经济不确定性和美国经济政策的不确定性会影响短期市场风险。因此,我们认为加密货币市场的风险-关联性溢出效应包含永久性或暂时性的冲击变化。此外,长期溢出效应的低值结果表明,加密货币市场的风险冲击并非永久性的,这表明长期变化需要对市场动态进行仔细监测和控制。此外,我们观察到,加密货币市场的高风险溢出与一些事件发生的时期相吻合,如 2018 年 1 月的中美贸易紧张局势、2019 年 2 月的英国脱欧进程和 2019 年 11 月的 COVID-19 爆发。接下来,我们观察到加密货币市场风险溢出效应在 2020 年 3 月后有所下降。市场风险溢出减轻的原因可能是美联储的量化宽松信号启动了市场的放松过程。因为美联储在 2022 年 3 月发出的对抗通胀的信号也恰好是加密货币市场风险溢出增加的时期。基于此,我们提出证据表明,美联储与市场的沟通机制有可能影响短期和长期预期。在这种情况下,我们可以说,我们关于新闻的不确定性会导致短期风险增加的假设得到了证实。我们的研究结果可能会对投资组合经理和广大投资者降低金融风险的投资政策产生影响。原创性/价值我们的论文通过研究主要加密货币之间的相互联系及其背后的驱动因素,特别是关注基于新闻的经济不确定性的作用,为相关文献做出了贡献。更广泛地说,我们计算了先进方法的使用情况,并纳入了实时经济不确定性数据,从而提高了研究的原创性和价值,为加密货币市场的动态提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Managerial Finance
Managerial Finance BUSINESS, FINANCE-
CiteScore
3.30
自引率
12.50%
发文量
103
期刊介绍: Managerial Finance provides an international forum for the publication of high quality and topical research in the area of finance, such as corporate finance, financial management, financial markets and institutions, international finance, banking, insurance and risk management, real estate and financial education. Theoretical and empirical research is welcome as well as cross-disciplinary work, such as papers investigating the relationship of finance with other sectors.
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