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

2025-03-03

AI Summary

This study investigates risk spillovers and cross-market hedging strategies among AI-themed ETFs, AI tokens, and green markets (clean energy equities and green bonds). Employing time-varying transmission connectivity indices (TCI), R²-based variance decomposition, and multivariate volatility–correlation models, we systematically identify AI tokens as net risk receivers—revealing their systemic vulnerability—for the first time. In contrast, AI ETFs and clean energy stocks emerge as primary net risk transmitters, while green bonds serve as secondary risk absorbers. We further construct a minimum-correlation multi-asset portfolio, which significantly outperforms conventional minimum-variance and minimum-connectivity portfolios in mitigating spillover risk: it reduces exposure to AI token volatility, improves the Sharpe ratio by 18.3%, and enhances hedging efficiency by 22.7%. The findings establish a novel paradigm for risk management and asset allocation at the intersection of AI finance and sustainable finance.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Security and privacy of machine learning and AI applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Analyzes risk spillovers among AI ETFs, AI tokens, and green markets.
• Identifies AI ETFs and clean energy as risk transmitters.
• Evaluates hedging effectiveness and portfolio strategies for AI tokens.

Innovation

• R2 decomposition method analyzes risk spillovers
• AI ETFs and clean energy transmit risks
• Multivariate portfolios reduce AI tokens risk