Shupei Huang

The Multiscale Conformation Evolution of the Financial Time Series

Fluctuations of the nonlinear time series are driven by the traverses of multiscale conformations from one state to another. Aiming to characterize the evolution of multiscale conformations with changes in time and frequency domains, we present an algorithm that combines the wavelet transform and the complex network. Based on defining the multiscale conformation using a set of fluctuation states from different frequency components at each time point rather than the single observable value, we construct the conformational evolution complex network. To illustrate, using data of Shanghai’s composition index with daily frequency from 1991 to 2014 as an example, we find that a few major conformations are the main contributors of evolution progress, the whole conformational evolution network has a clustering effect, and there is a turning point when the size of the chain of multiscale conformations is 14. This work presents a refined perspective into underlying fluctuation features of financial markets.

Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

Reconstructing time series into a complex network to assess the evolution dynamics of the correlations among energy prices

Abstract Reconstructing a time series into a complex network can help uncover the dynamic information hidden in the time series. Previous studies mainly focused on the long-term relationship between two energy prices, and traditional econometric methods poorly reflect the evolution of correlations among variables from a short-term perspective. Thus, first, we divide natural gas, coal and crude oil price time series into a series of segments via a set of temporal sliding windows and then calculate the correlation coefficients for each pair of energy prices in each segment. Second, we define the correlation modes based on the correlation coefficients and a coarse graining process. Third, we reconstruct the time series into a complex network to assess the evolution dynamics of the correlations among energy prices. The results show that a few major correlation modes and transmission patterns play important roles in the evolution. The evolution of the correlation modes among energy prices exhibits a significant cluster effect. Approximately 30 days is a turning point at which one type of cluster transforms into another type. Then, we improve the betweenness centrality algorithm to measure the media capability of the correlation mode in the evolution process of different clusters. Based on the transmission probabilities between clusters, we can determine the evolution direction of the correlation modes based on energy prices. These results are useful for monitoring fluctuations in energy prices and making decisions for risk avoidance.