基于俄乌冲突的能源市场与关联资产之间的时变依赖性研究

Chen Yan, Shi Zhun
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引用次数: 0

摘要

能源行业对俄乌冲突导致的地缘政治变化极为敏感,全球能源市场持续动荡,重塑了全球能源供应动态,并极大地影响了全球贸易模式。本研究利用静态和动态 GARCH-Copula 模型,阐明了能源市场与相关资产之间的依赖关系。与多变量 GARCH 模型相比,Copula 函数具有明显的优势,特别是在划分联合资产分布、捕捉市场依赖的非线性特征以及突出稳健的尾部相关结构方面。除了市场间的平均依赖性外,其尾部相关性还为市场风险提供了一个重要的视角。本研究深入探讨了能源市场和相关资产之间相互依赖的时间和结构变化。它探究了动态相互依存的潜在结构断点,并指出了它们的出现。通过聚焦俄罗斯-乌克兰冲突,本研究提供了能源市场与其他资产类别之间不断变化的相互作用的整体视角,为投资者的投资组合优化、监管监督和风险缓解提供了至关重要的见解。此外,本研究采用小波分析法,研究了能源市场与相关资产之间相互依存关系的频域特征。随着频率的减弱,市场价格波动变得不那么明显。连续小波功率谱显示,价格变化主要是中高频率的。交叉小波变换结果表明,能源市场与相关资产之间的相关性受短期扰动的影响要大于持久变化。
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A study on time-varying dependence between energy markets and linked assets based on the Russia-Ukraine conflict
The energy industry, acutely sensitive to geopolitical shifts due to the Russia-Ukraine conflict, experiences sustained disturbances in global energy markets, reshaping global energy supply dynamics and significantly influencing global trade patterns. Utilizing static and dynamic GARCH-Copula models, this study elucidates the dependency between energy markets and related assets. The Copula function, when compared with the multivariate GARCH model, demonstrates distinct advantages, notably in delineating joint asset distributions, capturing market dependence's nonlinear traits, and highlighting robust tail correlation structures. Beyond the average inter-market dependence, its tail correlation offers a vital perspective on market risk. This research delves into the temporal and structural variations in interdependence between energy markets and related assets. It probes potential structural breakpoints in dynamic interdependence and pinpoints their occurrences. By focusing on the Russia-Ukraine conflict, this study offers a holistic view of the changing interplay between the energy market and other asset categories, providing pivotal insights for investor portfolio optimization, regulatory oversight, and risk mitigation. Moreover, employing wavelet analysis, this study examines the frequency domain traits of the interdependency between energy markets and associated assets. As frequency wanes, market price fluctuations become less pronounced. The continuous wavelet power spectrum indicates that price variations are predominantly mid to high frequency. Cross-wavelet transform results suggest that correlations between energy markets and related assets are more influenced by short-term perturbations than enduring shifts.
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