Monitoring the Dynamic Networks of Stock Returns with an Application to the Swedish Stock Market

In this paper, two approaches for measuring the distance between stock returns and the network connectedness are presented that are based on the Pearson correlation coefficient dissimilarity and the generalized variance decomposition dissimilarity. Using these two procedures, the center of the network is determined. Also, hierarchical clustering methods are used to divide the dense networks into sparse trees, which provide us with information about how the companies of a financial market are related to each other. We implement the derived theoretical results to study the dynamic connectedness between the companies in the Swedish capital market by considering 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain a one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method from statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.