Research in International Business and Finance, Год журнала: 2024, Номер 75, С. 102718 - 102718
Опубликована: Дек. 25, 2024
Язык: Английский
Research in International Business and Finance, Год журнала: 2024, Номер 75, С. 102718 - 102718
Опубликована: Дек. 25, 2024
Язык: Английский
Global Finance Journal, Год журнала: 2025, Номер 64, С. 101076 - 101076
Опубликована: Янв. 11, 2025
Язык: Английский
Процитировано
0International Journal of Islamic and Middle Eastern Finance and Management, Год журнала: 2025, Номер unknown
Опубликована: Янв. 20, 2025
Purpose This study uses the time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness approach to examine interconnectedness between artificial intelligence (AI)-related financial assets and Islamic banking indices in markets. It reveals linkages across different market segments their influence on spillovers at investment horizons. Design/methodology/approach The research methodology involves using TVP-VAR model. model allows authors analyze return time frames by capturing dynamic nature of relationships variables. also consider various global factors regression analysis for rigor (Chatziantoniou et al. , 2023). Findings shows that short-term changes impact extreme risk more than medium- or long-term changes. Well-established like AI-related stocks (MSFT, GOOG NVDA) banks (Saudi Arabia, UAE) consistently contribute transmit returns. In contrast, most tokens Asian tend receive shocks. Two related gold uncertainty US dollar demonstrate potential hedging predictability interconnectedness. Practical implications results emphasize vital role diversifying a portfolio managing risks, providing valuable insights analysts professionals finance, management. Originality/value rising importance investing has raised concerns about compatibility with traditional instruments, especially finance (Rabbani 2023; Darehshiri 2022; Yousaf 2022). paper examines connections among stocks, shed light correlations impacts landscape.
Язык: Английский
Процитировано
0Economics, Год журнала: 2025, Номер 19(1)
Опубликована: Янв. 1, 2025
Abstract In recent decades, the rising challenges posed by climate change have prompted investors to take a keen interest in green assets and incorporate them into their portfolios achieve optimal returns. Therefore, this article explores static dynamic connectedness between renewable energy stocks (solar, wind, geothermal), cryptocurrencies (Stellar, Nano, Cardona, IOTA), agricultural commodities (wheat, cocoa, coffee, corn, cotton, sugar, soybean) using TVP-VAR (time-varying parameter vector autoregression) framework offering novel empirical evidence for portfolio managers. The is examined across two distinct sub-samples: during COVID-19 post-COVID-19 times. Because relevant can implications diversification benefits, we proceed with computation of weights, hedge ratios, effectiveness DCC-GARCH model. main findings are as follows: We first find that particularly Cardona Stellar exhibit highest spillovers network wind stock has least other markets. Second, NET spillover indices reveal coffee consistently net receivers over entire period except beginning pandemic. Third, diverse positions implying impact pandemic varied significantly sectors. Finally, commodity depicts greater weights under scoring benefit diversified consisting agriculture assets.
Язык: Английский
Процитировано
0Cogent Business & Management, Год журнала: 2024, Номер 11(1)
Опубликована: Сен. 6, 2024
Язык: Английский
Процитировано
0Research in International Business and Finance, Год журнала: 2024, Номер 75, С. 102718 - 102718
Опубликована: Дек. 25, 2024
Язык: Английский
Процитировано
0