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A network analysis of the volatility of high dimensional financial series

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  • Matteo Barigozzi
  • Marc Hallin

Abstract

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.
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  • Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
  • Handle: RePEc:bla:jorssc:v:66:y:2017:i:3:p:581-605
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    File URL: http://hdl.handle.net/10.1111/rssc.12177
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    Cited by:

    1. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
    2. repec:eee:econom:v:206:y:2018:i:1:p:187-225 is not listed on IDEAS
    3. Gross, Christian & Siklos, Pierre, 2018. "Analyzing credit risk transmission to the non-financial sector in Europe: a network approach," ESRB Working Paper Series 78, European Systemic Risk Board.
    4. repec:cup:jfinqa:v:53:y:2018:i:03:p:1371-1390_00 is not listed on IDEAS
    5. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers 257939806, Lancaster University Management School, Economics Department.
    6. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(03), pages 1371-1390, June.
    7. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. " Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach," Working Papers Department of Decision Sciences and Information Management 590528, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.
    8. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.

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    JEL classification:

    • F3 - International Economics - - International Finance
    • G3 - Financial Economics - - Corporate Finance and Governance

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