Evolution of financial network through non-linear coupling of time series. (27th September 2018) - Lui, Ga Ching; Szeto, Kwok Yip
Abstract: The structure of financial market is captured using an analysis of non-linear coupling between various stocks using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since the coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series is generally non-linear and time dependent, which would not be well captured with correlation measurements. Our time warping method provides an alternative to overcome this problem and we apply dTWGA on Dow Jones Index and Hang Seng Index and their constituent stocks. Through dTWGA, the coupling between the stock time series provides a network description of the financial market. We perform different measurements of the resultant financial networks to observe the evolution of their topological structure. We observe consistent major topological changes during market crashes, leading to a significant decrease in the size of the network. We expect these technical analyses provide new insights into the systemic risk of financial market in the perspective of the stability of the corresponding network.