A network analysis of the volatility of high dimensional financial series. Issue 3 (17th September 2016)

• Record Type: Journal Article
• Authors: Barigozzi, Matteo; Hallin, Marc
• Is Part Of: Journal of the Royal Statistical Society. Volume 66:Issue 3(2017:May)
• Page Start: 581
• Page End: 605
• Publication Date: 2016-09-17
• Subjects: Dynamic factor models -- Sparse vector auto‐regression models -- Standard & Poor's 100 index -- Systemic risk -- Volatility; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12177
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: Interconnectedness between stocks and firms plays a crucial role in the volatility contagion phenomena that characterize financial crises, and graphs are a natural tool in their analysis. We propose graphical methods for an analysis of volatility interconnections in the Standard & Poor's 100 data set during the period 2000–2013, which contains the 2007–2008 Great Financial Crisis. The challenges are twofold: first, volatilities are not directly observed and must 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 auto‐regressive model. The inversion of this auto‐regression, 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.