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

Duration: 37 mins 32 secs

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Description:	Barigozzi, M (London School of Economics) Thursday 25th August 2016 - 13:30 to 14:10


Created:	2016-08-31 17:21
Collection:	Theoretical Foundations for Statistical Network Analysis
Publisher:	Isaac Newton Institute
Copyright:	Barigozzi, M
Language:	eng (English)


Abstract:	Co-author: Marc Hallin (ECARES-ULB ) We consider weighted directed networks for analysing large panels of financial volatilities.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 innovations. To challenge the curse of dimensionality, we decompose the panel into a factor (market) driven component and an idiosyncratic one modelled by means of a sparse VAR. Inversion of the VAR together with suitable identification restrictions, produce the estimated network, bymeans of which we can assess how systemic each firm is. An analysis of the U.S. stock market demonstrates the prominent role of Financial firms as source of contagion during the 2007-2008 crisis.

Abstract:

Co-author: Marc Hallin (ECARES-ULB )

We consider weighted directed networks for analysing large panels of financial volatilities.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 innovations. To challenge the curse of dimensionality, we decompose the panel into a factor (market) driven component and an idiosyncratic one modelled by means of a sparse VAR. Inversion of the VAR together with suitable identification restrictions, produce the estimated network, bymeans of which we can assess how systemic each firm is. An analysis of the U.S. stock market demonstrates the prominent role of Financial firms as source of contagion during the 2007-2008 crisis.

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