Economic Analysis and Policy, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Economic Analysis and Policy, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Energy Economics, Год журнала: 2024, Номер 136, С. 107732 - 107732
Опубликована: Июнь 26, 2024
Язык: Английский
Процитировано
8Energy Economics, Год журнала: 2024, Номер unknown, С. 108055 - 108055
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
4International Review of Economics & Finance, Год журнала: 2025, Номер unknown, С. 103872 - 103872
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Financial Innovation, Год журнала: 2025, Номер 11(1)
Опубликована: Янв. 21, 2025
Abstract This study assessed the connectedness between oil shocks and industry stock indexes in United States (US). We consider normal extreme conditions across different frequency horizons, quantile time–frequency method is used to determine tail risk contagion under horizons. Our results reveal that short-term significantly exceeds long-term connectedness. also indicate lower upper quantiles greater than at conditional mean. Importantly, shock biggest net transmitter of US sectors conditions, highlighting cause substantial variations sector returns short, medium, long term. Finally, QAR(3) model demonstrates significant impact on during conditions. Therefore, our underscores role asymmetry reaction oil-related shocks, we suggest policies aimed overcoming adverse effects markets promoting financial stability should incorporate asymmetric features.
Язык: Английский
Процитировано
0The Quarterly Review of Economics and Finance, Год журнала: 2025, Номер 100, С. 101974 - 101974
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Journal of Financial Economic Policy, Год журнала: 2025, Номер unknown
Опубликована: Март 7, 2025
Purpose This paper aims to examine the extreme return spillover between crude oil and ESG stocks for 10 developed 11 emerging economies from 4 January 2016 3 October 2024. Design/methodology/approach The extends generalized VAR methodology proposed by Diebold Yilmaz (2012) (DY12) quantify dynamics of spillovers across indices oil. authors use quantile connectedness approach Ando et al. (2022) explore with various quantiles (q), such as bearish, normal bullish market conditions. Findings critical findings are follows: firstly, study reports at tails, especially during COVID-19, resulting in asymmetry tail dependency within network. Secondly, dependence is maximum COVID-19. Thirdly, acts a major recipient, but degree receiving shocks innovations intensifies Lastly, network analysis depicts complex bearish phase mainly markets. Originality/value Unlike previous studies which uses vector autoregression (VAR) models, cointegration methods, wavelet analysis, cross-correlation techniques, copula approaches GARCH models fails capture under conditions derived forecast-error variance decomposition account tail-specific dynamics, this offers more comprehensive understanding effects using median-based (QVAR) indices, tested
Язык: Английский
Процитировано
0International Review of Economics & Finance, Год журнала: 2024, Номер 96, С. 103533 - 103533
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
2International Economics, Год журнала: 2024, Номер 180, С. 100554 - 100554
Опубликована: Окт. 2, 2024
Язык: Английский
Процитировано
2Economic Analysis and Policy, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
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