Spillover Effects of International Crude Oil, China’s Carbon Trading, and New Energy Market Prices From a Time–Frequency Perspective
国際原油、中国炭素取引、新エネルギー市場価格の時間・周波数領域における波及効果 (AI 翻訳)
Yulin Li, Yongwen Liu, Yue Li
🤖 gxceed AI 要約
日本語
本論文は、2018年3月から2024年4月までのデータを用い、分位点回帰とTVP-VARモデルを周波数領域に拡張し、原油価格、炭素排出権価格、新エネルギー株価の間の波及効果を分析。通常時と極端な市場条件下での波及の非対称性や時間・周波数依存性を明らかにし、特に新エネルギー車セクターが情報送信者として支配的であることを示した。
English
Using quantile regression and TVP-VAR extended to frequency domain, this study analyzes spillover effects among crude oil, China's carbon market, and new energy stocks (March 2018-April 2024). It finds asymmetric spillovers under extreme conditions, with short-term (within 1 week) information transmission. The downstream NEV sector dominates as net information transmitter under both normal and extreme conditions.
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
中国の炭素市場（全国排出権取引）と新エネルギー市場の連動性を分析しており、日本でも2023年から開始された排出権取引市場や、証券市場における気候関連情報開示の議論に示唆を与える。特に炭素価格と新エネルギー株価の波及経路の理解は、日本の投資家や企業のリスク管理に貢献しうる。
In the global GX context
This paper provides empirical evidence on the interconnectedness of carbon pricing and new energy markets, relevant to global carbon market design and the understanding of transition risk. Its methodological approach (time-frequency spillover analysis) can be applied to other markets, including the EU-ETS and emerging carbon markets in Asia.
👥 読者別の含意
🔬研究者:Offers a novel time-frequency spillover framework for carbon and energy markets, useful for researchers studying market interactions and information transmission.
🏢実務担当者:Helps corporate sustainability teams understand how carbon prices and new energy stock fluctuations interact, informing risk assessment and investment strategies.
🏛政策担当者:Provides insights for carbon market regulators on how external shocks (e.g., oil price changes) affect carbon prices, aiding in market stability measures.
📄 Abstract（原文）
The intricate financial relationships among crude oil prices, carbon emission rights, and new energy stocks warrant thorough investigation, as fossil fuel consumption, carbon emissions, and the development of new energy are fundamental pillars of global environmental sustainability. This article combines quantile regression with the TVP‐vector autoregressive (VAR) model, extending it to the frequency domain, to systematically analyze the spillover effects among the carbon emission market, crude oil spot prices, and the new energy stock market, with a particular focus on the internal spillovers across the upstream, midstream, and downstream sectors of the new energy market itself, from March 12, 2018, to April 9, 2024, under both normal (median quantile) and extreme market conditions. The findings reveal that, under normal market conditions, the new energy market primarily acts as an information transmitter. In contrast, during extreme market conditions, spillover effects intensify, with stronger right‐tail asymmetry than left‐tail. The dynamic analysis shows that spillover effects vary overtime, particularly during major exogenous shocks. Additionally, total, net, and pairwise spillovers are concentrated in the short‐term frequency (within 1 week), indicating the rapid transmission of information. Furthermore, under extreme conditions, the upstream new energy sector consistently serves as the information sender in both time and frequency dimensions, while the crude oil market (Dtd) remains the primary information receiver. The carbon market (WEA) also transmits information, but with lower intensity. Most importantly, the downstream new energy vehicle (NEV) market dominates as the net information transmitter under both normal and extreme conditions, in both temporal and frequency domains. These findings offer novel insights into the structural evolution of information transmission mechanisms in the ongoing global transition to a low‐carbon economy.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1155/er/9304542first seen 2026-05-15 17:02:59
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。