Co-movements of NFTs, DeFi tokens and carbon ETFs: nonlinear dynamics and sustainable portfolio implications
NFT、DeFiトークン、カーボンETFの共変動:非線形ダイナミクスとサステナブルポートフォリオへの示唆 (AI 翻訳)
R. Katoch, Samoon Khan
🤖 gxceed AI 要約
日本語
本研究は、NFT、DeFiトークン、カーボンETFの共変動と非線形ダイナミクスをウェーブレット分析により実証。COVID-19パンデミックなどの危機時にこれらの資産間の連動が強まり、カーボンETFが長期的な分散投資に有効であることを示した。環境配慮型投資家向けに、デジタル資産とカーボン市場を組み合わせたポートフォリオ構築の定量的根拠を提供する。
English
This study empirically analyzes co-movements and nonlinear dynamics among NFTs, DeFi tokens, and carbon ETFs using wavelet coherence. It finds that correlations intensify during global crises like COVID-19, and carbon ETFs serve as effective long-term diversifiers. The results provide a quantitative foundation for constructing sustainable portfolios that integrate digital assets and carbon markets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではカーボンETF市場は発展途上だが、本研究成果はGX投資の分散戦略に示唆を与える。日本企業がグローバルなカーボン市場を活用したサステナブルなポートフォリオ構築を検討する際の参考となる。
In the global GX context
As carbon ETFs become key instruments in transition finance, this paper offers novel insights into their interaction with digital assets. The findings help global investors understand how carbon markets can hedge against volatility in emerging crypto markets, supporting climate-aligned portfolio strategies.
👥 読者別の含意
🔬研究者:Provides a wavelet-based framework for analyzing nonlinear dynamics between carbon ETFs and digital assets, useful for further studies in climate finance.
🏢実務担当者:Portfolio managers can use the findings to integrate carbon ETFs into digital asset portfolios for better risk management and sustainability alignment.
📄 Abstract(原文)
The primary purpose of this research is to empirically analyze the co-movement, nonlinear dynamics, and spillover effects among non-fungible tokens (NFTs) and decentralized finance (DeFi) tokens, carbon exchange-traded funds (ETFs). The study aims to quantify these interactions, especially during major global crises, to derive practical implications for constructing sustainable and diversified investment portfolios. It seeks to provide a quantitative foundation for environmentally conscious investors to navigate the risks and opportunities at the intersection of digital finance and sustainability, addressing a significant gap in the existing literature. This study employs a quantitative approach using advanced econometric models to analyze the daily returns of NFTs, DeFi tokens and Carbon ETFs. The methodology is centered on time-frequency analysis to capture dynamic relationships. Key methods include wavelet coherence (WTC) to identify co-movements across different time scales, partial wavelet coherence (PWC) to isolate direct linkages by controlling for systemic factors and wavelet correlation to examine how these relationships evolve over various investment horizons. This robust framework moves beyond traditional linear models to analyze complex, nonlinear market dynamics. The relationship between digital assets and carbon ETFs is profoundly dynamic, event-driven and frequency-dependent. Co-movements, weak in the short term, intensify dramatically during global crises like the COVID-19 pandemic and geopolitical conflicts. The correlation strengthens progressively as the investment horizon lengthens, indicating carbon ETFs serve as a strong proxy for long-term systemic factors. PWC analysis confirms these are genuine, direct linkages, not merely spurious correlations, highlighting the true interconnectedness of these markets during periods of global instability. This study is limited by its focus on a specific set of assets and a defined time period (2020–2024); therefore, findings may not be generalizable to all market conditions or digital assets. The use of CRBN and SMOG as proxies for the carbon market may not capture all nuances of environmental finance. Future research could expand this framework by incorporating other financial markets, such as bonds and commodities, or by applying regime-switching models like SETAR to further explore nonlinear dynamics and enhance the robustness of the findings. For environmentally conscious investors, this study provides a quantitative foundation for building climate-aligned portfolios. The findings demonstrate that integrating carbon ETFs into a digital asset portfolio is a sound risk management strategy that enhances diversification and hedges against both market volatility and potential regulatory risks tied to blockchain’s carbon footprint. The results suggest a strategic allocation approach: utilizing stablecoins as portfolio anchors, carefully managing exposure to central shock transmitters and incorporating carbon ETFs for long-term stability and hedging. This research provides a data-driven roadmap for aligning the burgeoning field of digital finance with pressing sustainability goals. By demonstrating how to construct portfolios that are both financially robust and environmentally responsible, it addresses the significant environmental concerns surrounding blockchain technology. This contributes to a more sustainable financial ecosystem, offering a pathway for investors to participate in innovative digital asset markets while actively managing and hedging against their carbon footprint, thereby promoting greater corporate and social responsibility in finance. This paper’s originality lies in its comprehensive empirical analysis of the co-movement and nonlinear dynamics among the specific triad of NFTs, DeFi tokens and carbon ETFs – an intersection that remains largely unexplored. By applying advanced wavelet-based methodologies, the study provides novel, actionable insights into the event-driven and frequency-dependent nature of their interconnectedness. It successfully bridges the gap between digital finance and sustainability, offering a unique, data-driven framework for constructing resilient, next-generation portfolios that are both financially sound and environmentally conscious.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/mf-09-2025-0740first seen 2026-05-15 21:34:12
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