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Estimating Global Bank Network Connectedness

Author

Listed:
  • Mert Demirer

    (MIT)

  • Francis X. Diebold

    () (University of Pennsylvania)

  • Laura Liu

    (University of Pennsylvania)

  • Kamil Yilmaz

    (Koc University)

Abstract

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statistically, we find that global banking connectedness is clearly linked to bank location, not bank assets. Dynamically, we find that global banking connectedness displays both secular and cyclical variation. The secular variation corresponds to gradual increases/decreases during episodes of gradual increases/decreases in global market integration. The cyclical variation corresponds to sharp increases during crises, involving mostly cross-country, as opposed to within-country, bank linkages.

Suggested Citation

  • Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 1512, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1512
    as

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    File URL: http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1512.pdf
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    2. FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    3. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    4. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    5. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    6. Matteo Barigozzi & Christian T. Brownlees, 2013. "Nets: Network estimation for time series," Economics Working Papers 1391, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    8. G.M. Constantinides & M. Harris & R. M. Stulz (ed.), 2013. "Handbook of the Economics of Finance," Handbook of the Economics of Finance, Elsevier, volume 2, number 2-b.
    9. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    10. Gorton, Gary B., 2015. "The Maze of Banking: History, Theory, Crisis," OUP Catalogue, Oxford University Press, number 9780190204839.
    11. Viral Acharya & Robert Engle & Matthew Richardson, 2012. "Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks," American Economic Review, American Economic Association, vol. 102(3), pages 59-64, May.
    12. G.M. Constantinides & M. Harris & R. M. Stulz (ed.), 2013. "Handbook of the Economics of Finance," Handbook of the Economics of Finance, Elsevier, volume 2, number 2-a.
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    Citations

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    Cited by:

    1. Thiem, Christopher, 2018. "Cross-category spillovers of economic policy uncertainty," Ruhr Economic Papers 744, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Gorkem Bostanci & Kamil Yilmaz, 2015. "How Connected is the Global Sovereign Credit Risk Network?," Koç University-TUSIAD Economic Research Forum Working Papers 1515, Koc University-TUSIAD Economic Research Forum.
    3. Kamil Yilmaz, 2018. "Bank Volatility Connectedness in South East Asia," Koç University-TUSIAD Economic Research Forum Working Papers 1807, Koc University-TUSIAD Economic Research Forum.
    4. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
    5. Mert Demirer & Umut Gokcen & Kamil Yilmaz, 2018. "Financial Sector Volatility Connectedness and Equity Returns," Koç University-TUSIAD Economic Research Forum Working Papers 1803, Koc University-TUSIAD Economic Research Forum.
    6. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised May 2018.
    7. Mardi Dungey & John Harvey & Pierre Siklos & Vladimir Volkov, 2017. "Signed spillover effects building on historical decompositions," CAMA Working Papers 2017-52, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Georgios Moratis & Plutarchos Sakellaris, 2017. "Measuring the systemic importance of banks," Working Papers 240, Bank of Greece.
    9. Sachapon Tungsong & Fabio Caccioli & Tomaso Aste, 2017. "Relation between regional uncertainty spillovers in the global banking system," Papers 1702.05944, arXiv.org.
    10. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series,in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.), Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136 Central Bank of Chile.
    11. Dungey, Mardi & Harvey, John & Volkov, Vladimir, 2017. "The changing international network of sovereign debt and financial institutions," Working Papers 2017-04, University of Tasmania, Tasmanian School of Business and Economics.
    12. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks; Potential Pitfalls and a Simple Solution," IMF Working Papers 17/107, International Monetary Fund.
    13. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    14. Grant, Everett & Yung, Julieta, 2017. "The Double-Edged Sword of Global Integration: Robustness, Fragility & Contagion in the International Firm Network," Globalization and Monetary Policy Institute Working Paper 313, Federal Reserve Bank of Dallas.
    15. Christian Gross & Pierre L. Siklos, 2018. "Analyzing Credit Risk Transmission to the Non-Financial Sector in Europe: A Network Approach," CQE Working Papers 7218, Center for Quantitative Economics (CQE), University of Muenster.
    16. Sheheryar Malik & TengTeng Xu, 2017. "Interconnectedness of Global Systemically-Important Banks and Insurers," IMF Working Papers 17/210, International Monetary Fund.

    More about this item

    Keywords

    Systemic risk; connectedness; systemically important financial institutions; vector autoregression; variance decomposition; lasso; elastic net; adaptive lasso; adaptive elastic net.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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