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原油和股票的关系

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中文导读

原油和股票市场中均存在多重目标不同的市场参与者,而这些参与者的活动时间周期具有较大差异,例如,政府关注长期市场均衡,而金融投资者倾向于在较短的时间内获得更高的利润,这就使得原油和股票市场以及两个市场间的互动具有显著的多时间尺度特征,这也是造成两市场间互动具有高度复杂性和非线性特征等不可忽视的原因。因此,从多时间尺度视角,探析原油与股票市场间动态关系的演化,能够为该领域提供更为深入细致的观察。今日小编推荐《Unveiling heterogeneities of relations between the entire oil–stock interaction and its components across time scales》一文,该文从多时间尺度的视角,对原油与股票市场间动态关系的决定要素及其异质性展开了研究。

01

            主要亮点

1.从多时间尺度视角,研究了原油-股票市场间动态关系的决定要素。

2.长时间尺度要素对原油-股票市场间动态关系的结构变化具有决定作用。

3.短时间尺度要素能够有效捕捉原油-股票市场间动态关系的波动变化。


02

            原文摘要

原油与股票市场间动态关系的复杂性和非线性特征与两市场中来自于多时间尺度要素间的错综关系紧密联系。然而,针对原油-股票市场间动态关系的多时间尺度要素对两市场间动态关系的影响鲜有深入研究。因此,本文拟挖掘在原油-股票市场动态关系中具有决定性的时间尺度要素及其随时间的演化。本文选取天频率下的Brent原油现货价格与摩根斯坦利的明晟指数作为样本数据,并采用小波变换、灰色关联分析及网络分析方法从静态和动态两个角度展开分析。主要研究结果如下:从静态角度来看,小波方差的变化说明长时间周期要素能够引起原油-股票市场间动态关系的结构变化;小波相关值的变化表明短时间尺度要素是引起原油-股票市场间动态关系瞬时波动的主要因素;而不同时间尺度要素与原油-股票市场间动态关系间不存在显著的领先滞后关系。从动态的角度来看,短时间尺度要素和长时间尺度要素均为原油-股票市场间动态关系的主要决定性因素,中时间尺度要素对两市场间动态关系影响较弱。而通过具有较高传导能力的长时间周期要素和短时间尺度要素的识别可以帮助进行原油-股票市场间动态关系的结构变化和波动状态的识别。

03

            原文信息


Abstract

The oil–stock interaction characterized by complexity and nonlinearity makes relevant research difficult; this is caused by the intricate components of the entire market from a variety of time horizons. However, the heterogeneous influence of the multiscale market components on the entire oil–stock interaction has still been covered. Our objective is to further explore that which time scale is more essential to the integrated market interaction and the dynamic evolution of decisive timescale over time. The Brent spot oil price and the Morgan Stanley Capital International world stock index on a daily frequency were selected as the sample data, and the wavelet transform, the gray correlation, and network analyses were applied succinctly to conduct holistic and dynamical analyses. The primary findings are as follows: The wavelet-decomposed results indicate that impacts of oil price shocks on theoil–stock nexus differ in the long- and short-terms. From the holistic aspect, the growing wavelet variance with time scales demonstrates that long-term changes could lead to structure changes in trend of original market interactions. The wavelet correlation proves that short-term components are dominant in the original interaction and capture the dynamic information effectively. There are no significant lead–lag relations between the original oil–stock interaction and its components. From the dynamic perspective, it is confirmed that components from both the long and short terms are determined. The low and high transmission ability could be helpful to discover the structure changes caused by long-term components and modes controlling more information associated with the short-term components, respectively. The clustering effect limits major modes into a small amount.

 

原文链接


https://www.sciencedirect.com/science/article/pii/S0140988316301979