持続可能な金融のための気候リスク・スピルオーバー測定：炭素・エネルギー・金属市場の分位点時間周波数多層ネットワーク分析

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:高次モーメントと分位点時間周波数を組み合わせた気候リスク・スピルオーバー計測手法として、システミックリスク研究に有用。

🏢実務担当者:炭素・エネルギー・金属価格の変動が自社の移行リスク・調達コストにどう波及するかの感応度把握に活用可能。

🏛政策担当者:気候リスクが市場間で伝播する経路とタイミングを踏まえ、移行政策・金融安定モニタリングの設計に示唆。

Global warming has made climate physical and transition risks systemic sources of financial instability and key challenges to sustainability. These risks can spread to carbon, energy, and metals markets, which are central to the low-carbon transition and sustainable finance. This paper, therefore, explores the Quantile Time-Frequency connections among climate physical risk, climate transition risk, and the carbon, energy, and metals markets, with the analysis conducted through the lens of higher-moment risks. Using climate physical and transition risk indices together with daily carbon, energy, and metals futures settlement prices spanning 1 July 2015 to 29 December 2023, we employ the GJRSK model to extract higher-moment risk measures and apply the Quantile Time-Frequency framework to examine spillover effects across return, volatility, skewness, and kurtosis dimensions under different quantiles over the short term and long term. Furthermore, we construct a multilayer network that integrates information on return, volatility, skewness, and kurtosis risks under diverse market conditions and measure the topological properties of this network. The results reveal the following key insights: First, higher-moment risk spillovers are pronounced, though their magnitude is lower than that of return and volatility spillovers. Second, return, skewness, and kurtosis spillovers are primarily short-term driven, while volatility spillovers are concentrated in the long run. Climate risks generally act as receivers, but they shift to transmitters during long-term market downturns. Finally, findings from the multilayer complex network analysis demonstrate pronounced differences in the structural configurations of the interconnected networks linking climate risks to the return, volatility, skewness, and kurtosis risks of the carbon, energy, and metals markets across varying conditional quantiles. These findings support the measurement and monitoring of climate-related systemic risk and provide implications for sustainable finance, carbon and energy transition policies, and cross-market risk governance.

• openalex https://doi.org/10.3390/su182010355first seen 2026-10-14 05:13:52