Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Almeida, J., Gaio, C. & Gonçalves, T. C. (2024). Sustainability or artificial intelligence? Returns and volatility connectedness in crypto assets. The Journal of Risk Finance.
Exportar Referência (IEEE) 
J. A. Almeida et al.,  "Sustainability or artificial intelligence? Returns and volatility connectedness in crypto assets", in The Journal of Risk Finance, 2024
Exportar BibTeX 
@article{almeida2024_1744889837994,
	author = "Almeida, J. and Gaio, C. and Gonçalves, T. C.",
	title = "Sustainability or artificial intelligence? Returns and volatility connectedness in crypto assets",
	journal = "The Journal of Risk Finance",
	year = "2024",
	volume = "",
	number = "",
	doi = "10.1108/JRF-04-2024-0111",
	url = "https://www.emerald.com/insight/publication/issn/1526-5943"
}
Exportar RIS 
TY  - JOUR
TI  - Sustainability or artificial intelligence? Returns and volatility connectedness in crypto assets
T2  - The Journal of Risk Finance
AU  - Almeida, J.
AU  - Gaio, C.
AU  - Gonçalves, T. C.
PY  - 2024
SN  - 1526-5943
DO  - 10.1108/JRF-04-2024-0111
UR  - https://www.emerald.com/insight/publication/issn/1526-5943
AB  - Purpose – This study aims to investigate the interconnectedness of sustainability-linked and AI-based
cryptocurrencies returns and volatility over five years (2018–2024). It aims to uncover the dynamic relationships between these two sectors under various market conditions, providing insights into their behavior and influence within the broader cryptocurrency market.
Design/methodology/approach – The research employs a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyze key cryptocurrencies associated with AI and sustainability. This approach is complemented by a quantile-based perspective, allowing for an in-depth examination of return and volatility spillovers across different market conditions. Thus, facilitating an understanding of the intricate dynamics between sustainability-linked and AI-based cryptocurrencies.
Findings – The findings reveal distinct market dynamics with the Sustainable sector consistently acting as a net transmitter, while the AI sector predominantly as a net receiver, indicating its reactive nature. In bearish markets, both sectors display high interconnectedness, with the Sustainable sector shaping dynamics. In bullish markets, the Sustainable sector maintains influence, while the AI sector adopts a more proactive role, influencing the market more than in bearish conditions. Post-Chat GPT 3 the Sustainable sector decreases influence, becoming a net receiver in bullish markets. In contrast, the AI sector strengthens as a net transmitter, signaling growing investor confidence and prominence.
Originality/value – This study explores the interconnectedness of sustainability-linked and AI-based
cryptocurrencies through a TVP-VAR model and a quantile-based analysis. It provides insights into how these sectors interact and influence each other across different market conditions, especially highlighting the significant shifts in dynamics following the advent of advanced technologies like Chat GPT 3. This contributes to a deeper understanding of the evolving landscape of the cryptocurrency market in the context of sustainability and AI.
ER  -