Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series

In this paper, we define weighted directed networks for large panels of financial time series wherethe edges and the associated weights are reflecting the dynamic conditional correlation structureof the panel. Those networks produce a most informative picture of the interconnections amongthe various series in the panel. In particular, we are combining this network-based analysis and ageneral dynamic factor decomposition in a study of the volatilities of the stocks of the Standard&Poor’s 100 index over the period 2000-2013. This approach allows us to decompose the panelinto two components which represent the two main sources of variation of financial time series:common or market shocks, and the stock-specific or idiosyncratic ones. While the common components,driven by market shocks, are related to the non-diversifiable or systematic components ofrisk, the idiosyncratic components show important interdependencies which are nicely describedthrough network structures. Those networks shed some light on the contagion phenomenons associatedwith financial crises, and help assessing how systemic a given firm is likely to be. We showhow to estimate them by combining dynamic principal components and sparse VAR techniques.The results provide evidence of high positive intra-sectoral and lower, but nevertheless quite important,negative inter-sectoral, dependencies, the Energy and Financials sectors being the mostinterconnected ones. In particular, the Financials stocks appear to be the most central vertices inthe network, making them the main source of contagion.