Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets
Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets
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DOI：
10.1103/physreve.87.012814
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发表时间：
2013-01-29
影响因子：
2.4
通讯作者：
Stanley, H. Eugene
中科院分区：
文献类型：
--
作者：
Zheng, Zeyu;Yamasaki, Kazuko;Stanley, H. Eugene
在一个高度相互依存的经济世界中，金融实体之间的关系的性质正在成为一个越来越重要的研究领域。最近，许多研究表明，最小生成树（MST）在提取金融实体之间的相互作用的有用性。在这里，我们提出了一种改进的MST网络，其度量距离是根据互相关系数的绝对值定义的，使网络相关实体之间的连接能够正确地表现出来。我们调查了69个每日时间序列，包括三种类型的金融资产：28个股票市场指标，21个货币期货和20个商品期货。我们发现，虽然由此产生的MST网络随着时间的推移而发展，但类似类型的金融资产往往具有随时间推移而稳定的连接。此外，我们发现股票市场指标的波动时间序列与欧盟二氧化碳排放限额（EUA）和原油期货（WTI）的波动时间序列之间存在特征时滞。这一时滞由波动性时间序列EUA（或WTI）与股市指标的互相关函数的峰值给出，并且与0明显不同（>20天），表明股市指标今天的波动性可以预测欧盟排放配额和原油在不久的将来的波动性。DOI：10.1103/PhysRevE.87.012814
In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO2 emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different (>20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future. DOI: 10.1103/PhysRevE.87.012814