Information flow between stock markets:A Koopman decomposition approach

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Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Exten-sive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.
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引言rn据中国地震台网中心测定,北京时间2022年1月8日01时45分,中国青海省海北州门源县发生了M6.9地震,震中位于( 37.77°N, 101.26°E),震源深度为10 km.在地震发生后,作者利用震源破裂过程、仪器观测数据资料,进一步考虑局部场地效应快速计算获得了此次地震的地震动强度图结果.震后快速产出的地震动强度图科技产品能够为震后的应急反应决策和地震应急评估提供科技支撑.
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