The quantile domain volatility shock transmission between carbon emission trading system and European emerging stock markets: Practical implications for portfolio optimization

排出権取引システムと欧州新興株式市場間の変動領域ボラティリティ・ショック伝達：ポートフォリオ最適化への実践的含意 (AI 翻訳)

Abdullah A. Aljughaiman, Mosab I. Tabash, Suzan Sameer Issa, Abdulateif A. Almulhim

PLoS ONE📚 査読済 / ジャーナル2026-06-08#炭素価格Origin: EU経営インパクト: 資金調達対象セクター: finance

原典: https://doi.org/10.1371/journal.pone.0349789

🤖 gxceed AI 要約

日本語

本論文は、EU排出権取引システム（EU-ETS）のボラティリティショックが欧州新興国株式市場に非対称に伝播することを分位点VARで実証。強気相場では伝達が88%に達し、ギリシャ・ポーランド・ハンガリーが脆弱である一方、スロバキアは低ベータのヘッジ資産として機能する。DCC-GARCH分析により、ポートフォリオ最適化においてチェコとスロバキア株式の組み入れが有効と示唆。

English

Using quantile VAR, this paper shows asymmetric volatility spillovers from EU-ETS to European emerging stock markets. In bullish conditions, transmission reaches 88%, with Greece, Poland, and Hungary most exposed, while Slovak equities act as a low-beta hedge. DCC-GARCH results suggest overweighting Czech and Slovak stocks in carbon-equity portfolios.

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

日本ではカーボンプライシングの本格運用が進む中、EU-ETSと株式市場の連関知見は国内投資家の国際分散投資リスク管理や、日本の排出権市場設計への示唆となる。とりわけ、ボラティリティの状態依存性を考慮したストレステストの重要性が示された。

In the global GX context

This study provides empirical evidence for how carbon market volatility transmits to equity markets, informing global transition finance risk management. It highlights the need for quantile-dependent stress testing in portfolio optimization, relevant for investors and regulators under ISSB/TCFD frameworks.

👥 読者別の含意

🔬研究者:Provides a quantile-based asymmetric spillover framework for carbon-stock market linkages, extending the literature on climate finance.

🏢実務担当者:Portfolio managers can use the findings to hedge carbon volatility risk by overweighting Slovak and Czech equities during EU-ETS turmoil.

🏛政策担当者:Regulators in carbon-exposed economies should incorporate quantile-dependent volatility loadings into financial stability stress tests.

📄 Abstract（原文）

This article explores the asymmetrical volatility shock spillovers between European Carbon Emission Trading System (EU-ETS) and European emerging economies’ stock markets by using the Quantile-based Vector Auto Regression (QVAR), “Extended Joint” connectivity framework. The QVAR approach suggested that a volatility shock in the EU-ETS transmitted higher conditional volatility shocks of 19.37%, 17.86%, 18.9%, 19.49% and 19.76% towards the equity markets of Czech Republic, Slovakia, Greece, Hungary and Poland at the bullish quantiles <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mtext>0</mml:mtext> </mml:mrow> <mml:mo>.</mml:mo> <mml:mrow> <mml:mtext>9</mml:mtext> </mml:mrow> <mml:mrow> <mml:mtext>5</mml:mtext> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> as compared with bearish <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mtext>0</mml:mtext> </mml:mrow> <mml:mo>.</mml:mo> <mml:mrow> <mml:mtext>0</mml:mtext> </mml:mrow> <mml:mrow> <mml:mtext>5</mml:mtext> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> and moderate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M3"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mtext>0</mml:mtext> </mml:mrow> <mml:mo>.</mml:mo> <mml:mrow> <mml:mtext>5</mml:mtext> </mml:mrow> <mml:mrow> <mml:mtext>0</mml:mtext> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> <mml:mtext> </mml:mtext> </mml:mrow> </mml:math> volatility conditions. The overall aggregated measure of the volatility shock spillovers between EU-ETS and equity markets is approximately 30.63% and 29.43% at bearish and moderate quantiles, respectively as compared with 88.01% at the bullish volatility conditions. The QVAR results also indicate that Greece, Poland, and Hungary (Slovakia) are structurally more (less) exposed to bearish and moderate EU-ETS volatility shocks. Therefore, Slovakia serves as a stabilizing asset within a multi-country portfolio, especially during bearish, bullish and moderate EU-ETS volatility conditions. Therefore, Slovakian equities remain least affected even in bullish EU-ETS volatility regime, functioning as an optimal low-beta hedge component and thereby help in reducing portfolio-wide shock amplification. Therefore, at the upper quantiles, where volatility transmission becomes uniformly elevated across all markets, fund managers take into account the volatility-convergence arbitrage, capitalizing on Slovakian equities persistent weaker sensitivity. However, stress testing frameworks should incorporate quantile-dependent country-specific volatility loadings, with higher coefficients for Greece, Poland, and Hungary across lower and moderate EU-ETS volatility shocks. In the terms of macro-prudential policy adjustments, equity market regulators and policy makers in Greece, Poland, and Hungary should strengthen transition-risk buffers during bearish and moderate ETS volatility shock diffusion. Finally, the DCC-GARCH- t copula findings suggested that portfolio managers can overweight Czech Republic and Slovakia equities during periods of rising EU-ETS volatility and use them as low-beta components in mixed carbon–equity portfolios.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.1371/journal.pone.0349789first seen 2026-06-10 05:04:20

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