A network analysis of the volatility of high dimensional financial series

Interconnectedness between stocks and firms plays a crucial role in the volatility contagion phenomena that characterise financial crises, and graphs are a natural tool in their analysis. In this paper, we are proposing graphical methods for an analysis of volatility interconnections in the Standard & Poor’s 100 dataset during the period 2000-2013, which contains the 2007-2008 Great Financial Crisis. The challenges are twofold: first, volatilities are not directly observed and have to be extracted from time series of stock returns; second, the observed series, with about 100 stocks, is high-dimensional, and curse of dimensionality problems are to be faced. To overcome this double challenge, we propose a dynamic factor model methodology, decomposing the panel into a factor-driven and an idiosyncratic component modelled as a sparse vector autoregressive model. The inversion of this autoregression, along with suitable identification constraints, produces networks in which, for a given horizon h, the weight associated with edge (i; j) represents the h-step-ahead forecast error variance of variable i accounted for by variable j’s innovations. Then, we show how those graphs yield an assessment of how systemic each firm is. They also demonstrate the prominent role of financial firms as sources of contagion during the 2007-2008 crisis.(This abstract was borrowed from another version of this item.)