Paper: arXiv 2503.01148

Authors: Ying-Hui Shao, Yan-Hong Yang, Han-Xian Zhou, Wei-Xing Zhou

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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a solid empirical analysis investigating risk spillovers and hedging strategies between AI assets (ETFs and tokens) and green markets using the R2 decomposition methodology. It provides practical insights for portfolio management by comparing minimum variance, minimum correlation, and minimum connectedness approaches across novel asset classes.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Analyze Risk Spillovers & Strategies<br>(AI ETFs, AI Tokens, Green Markets)"] --> B["Data Inputs:<br>AI ETFs, AI Tokens, Clean Energy,<br>& Green Bonds Market Data"]
    B --> C["Methodology:<br>R² Decomposition Method"]
    C --> D["Computational Process:<br>1. Transmission Connectedness (TCI)<br>2. Spillover Directions<br>3. Portfolio Optimization"]
    D --> E["Key Findings:<br>• AI ETFs & Clean Energy = Transmitters<br>• AI Tokens & Green Bonds = Receivers<br>• Min. Correlation Portfolio optimizes risk"]