Extreme downside risk connectedness and portfolio hedging among the G10 currencies

Emmanuel Joel Aikins Abakah , Mariem Brahim , Jean-Etienne Carlotti , Aviral Kumar Tiwari , Walid Mensi
{"title":"Extreme downside risk connectedness and portfolio hedging among the G10 currencies","authors":"Emmanuel Joel Aikins Abakah ,&nbsp;Mariem Brahim ,&nbsp;Jean-Etienne Carlotti ,&nbsp;Aviral Kumar Tiwari ,&nbsp;Walid Mensi","doi":"10.1016/j.inteco.2024.100503","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigates the frequency connectedness among foreign exchange markets of G10 countries, focusing on tail risk and its implications for portfolio management. To do so, we use a novel framework that combines the Conditional Autoregressive Value-at-Risk (CAViaR) model with the novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al. (2022) based on Baruník and Křehlík (2018) and Ando et al. (2018) approach. A key value of this paper to the literature is the provision of fresh empirical evidence on the extreme downside linkages among the markets examined. From the average connectedness measures, the top shock transmitters within the network were EUR, NOK, AUD, SEK, and NZD, while the main shock receivers emerged to be JPY and CHF, followed by CAD and GBP. We note that events in the major funding markets (the Eurozone, Japan) have a higher impact on the participants in these same markets than in relatively small markets (New Zealand, Norway). From the dynamic connectedness results, the magnitude of connectedness for the entire sample period increased during the COVID-19 era, compared to the magnitude before the COVID-19 outbreak. The cumulative spillover also reveals that USDNOK is the vastest net transmitter of spillovers to other markets, including SEK, CHF, and AUD. However, the EUR is the largest net beneficiary followed by JPY and CAD. Findings from the time-varying extreme downside analysis suggest that throughout the period, SEK and NOK are the other currencies' strongest and most frequent net spillover shock emitters for the short-, medium-, and long-term dynamics. Currency portfolio implications are discussed.</p></div>","PeriodicalId":13794,"journal":{"name":"International Economics","volume":"178 ","pages":"Article 100503"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Economics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S211070172400026X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This study investigates the frequency connectedness among foreign exchange markets of G10 countries, focusing on tail risk and its implications for portfolio management. To do so, we use a novel framework that combines the Conditional Autoregressive Value-at-Risk (CAViaR) model with the novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al. (2022) based on Baruník and Křehlík (2018) and Ando et al. (2018) approach. A key value of this paper to the literature is the provision of fresh empirical evidence on the extreme downside linkages among the markets examined. From the average connectedness measures, the top shock transmitters within the network were EUR, NOK, AUD, SEK, and NZD, while the main shock receivers emerged to be JPY and CHF, followed by CAD and GBP. We note that events in the major funding markets (the Eurozone, Japan) have a higher impact on the participants in these same markets than in relatively small markets (New Zealand, Norway). From the dynamic connectedness results, the magnitude of connectedness for the entire sample period increased during the COVID-19 era, compared to the magnitude before the COVID-19 outbreak. The cumulative spillover also reveals that USDNOK is the vastest net transmitter of spillovers to other markets, including SEK, CHF, and AUD. However, the EUR is the largest net beneficiary followed by JPY and CAD. Findings from the time-varying extreme downside analysis suggest that throughout the period, SEK and NOK are the other currencies' strongest and most frequent net spillover shock emitters for the short-, medium-, and long-term dynamics. Currency portfolio implications are discussed.

G10 货币之间的极端下行风险关联性和投资组合对冲
本研究调查了 G10 国家外汇市场的频率关联性,重点关注尾部风险及其对投资组合管理的影响。为此,我们使用了一个新颖的框架,该框架将条件自回归风险价值(CAViaR)模型与 Chatziantoniou 等人(2022 年)在 Baruník 和 Křehlík (2018 年) 和 Ando 等人(2018 年)方法基础上开发的新颖时变频率和量子关联性方法相结合。本文对文献的一个重要价值是提供了关于所研究市场之间极端下行联系的新经验证据。从平均连通性度量来看,网络内最大的冲击传播者是欧元、挪威克朗、澳元、瑞典克朗和新西兰元,而主要的冲击接收者是日元和瑞士法郎,其次是加元和英镑。我们注意到,与相对较小的市场(新西兰、挪威)相比,主要融资市场(欧元区、日本)发生的事件对这些市场参与者的影响更大。从动态关联度结果来看,与 COVID-19 爆发前的关联度相比,整个样本期间的关联度在 COVID-19 期间有所上升。累积溢出效应还显示,美元兑挪威克朗是向其他市场(包括瑞典克朗、瑞士法郎和澳元)溢出效应最大的净传播者。然而,欧元是最大的净受益者,其次是日元和加元。时变极端下行分析的结果表明,在整个期间,瑞典克朗和挪威克朗是其他货币在短期、中期和长期动态中最强和最频繁的净溢出冲击发射器。本文讨论了货币组合的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
International Economics
International Economics Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)
CiteScore
6.30
自引率
0.00%
发文量
74
审稿时长
71 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信