In this paper, we define weighted directed networks for large panels of financial time series where the edges and the associated weights are reflecting the dynamic conditional correlation structure of the panel. Those networks produce a most informative picture of the interconnections among the various series in the panel . In particular, we are combining this network-based analysis and a general 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 panel into 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 {\it systematic } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures. Those networks shed some light on the contagion phenomenons associated with financial crises, and help assessing how {\it systemic} a given firm islikely to be.We show how 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 most interconnected ones. In particular, the Financials stocks appear to be the most central vertices in the network, making them the main source of contagion .
We consider weighted directed networks for analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\&P100 index . In particular, we focus on the so-called {\it Long-Run Variance Decomposition Network } (LVDN), where the nodes are stocks, and the weight associated with edge (i,j) represents the proportion of h-step-ahead forecast error variance of variable i accounted for by variable j's innovations. To overcome the curse of dimensionality, we decompose the panel into a component driven by few global, market-wide, factors, and an idiosyncratic one modelled by means of a sparse vector autoregression (VAR) model. Inversion of the VAR together with suitable identification restrictions, produces the estimated network, by means of which we can assess how {\it systemic} each firm is.~Our analysis demonstrates the prominent role of financial firms as sources of contagion, especially during the~2007-2008 crisis .
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In this paper, we define
We consider
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large panels of financial time series where the edges and the associated weights are reflecting the dynamic conditional correlation structure of the panel. Those networks produce a most informative picture of the interconnections among the various series in the panel
analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\&P100 index
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are combining this network-based analysis and a general 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 panel into 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
focus on the so-called
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systematic
Long-Run Variance Decomposition Network
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components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures. Those networks shed some light on the contagion phenomenons associated with financial crises, and help assessing
(LVDN), where the nodes are stocks, and the weight associated with edge (i,j) represents the proportion of h-step-ahead forecast error variance of variable i accounted for by variable j's innovations. To overcome the curse of dimensionality, we decompose the panel into a component driven by few global, market-wide, factors, and an idiosyncratic one modelled by means of a sparse vector autoregression (VAR) model. Inversion of the VAR together with suitable identification restrictions, produces the estimated network, by means of which we can assess
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a given firm islikely to be.We show how 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 most interconnected ones. In particular, the Financials stocks appear to be the most central vertices in the network, making them the main source of contagion
each firm is.~Our analysis demonstrates the prominent role of financial firms as sources of contagion, especially during the~2007-2008 crisis