Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets

Ying-Hui Shao, Yan-Hong Yang and Wei-Xing Zhou

Papers from arXiv.org

Abstract: This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.

Date: 2025-03
New Economics Papers: this item is included in nep-ain and nep-tid
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2503.01148 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2503.01148

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().