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Monitoring banking system connectedness with big data

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  • Hale, Galina
  • Lopez, Jose A.

Abstract

In this paper, we propose a procedure that generates measures of connectedness between individual firms and for the system as a whole based on information observed only at the firm level; i.e., no explicit linkages are observed. We apply our procedure to large U.S. bank holding companies. We show how bank outcome variables of interest can be decomposed, including with mixed-frequency models, for network analysis to measure connectedness across firms. Network analysis of these decompositions produces measures that could be of use in financial stability monitoring as well as the analysis of individual firms’ linkages.

Suggested Citation

  • Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    • Handle: RePEc:eee:econom:v:212:y:2019:i:1:p:203-220
    DOI: 10.1016/j.jeconom.2019.04.027
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    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    3. Gara Afonso & Ricardo Lagos, 2015. "Trade Dynamics in the Market for Federal Funds," Econometrica, Econometric Society, vol. 83, pages 263-313, January.
    4. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    5. Berger, Allen N & Davies, Sally M & Flannery, Mark J, 2000. "Comparing Market and Supervisory Assessments of Bank Performance: Who Knows What When?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 641-667, August.
    6. Francis X. 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.
    7. Galina Hale & Mr. Tümer Kapan & Ms. Camelia Minoiu, 2016. "Crisis Transmission in the Global Banking Network," IMF Working Papers 2016/091, International Monetary Fund.
    8. 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.
    9. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    10. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    11. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    12. Thomas B. King & Daniel A. Nuxoll & Timothy J. Yeager, 2006. "Are the causes of bank distress changing? can researchers keep up?," Review, Federal Reserve Bank of St. Louis, vol. 88(Jan), pages 57-80.
    13. Eugene N. White, 2011. ""To Establish a More Effective Supervision of Banking": How the Birth of the Fed Altered Bank Supervision," NBER Working Papers 16825, National Bureau of Economic Research, Inc.
    14. Pavel Kapinos & Oscar A. Mitnik, 2016. "A Top-down Approach to Stress-testing Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(2), pages 229-264, June.
    15. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    16. Adam Ashcraft & James Mcandrews & David Skeie, 2011. "Precautionary Reserves and the Interbank Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(s2), pages 311-348, October.
    17. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    18. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 255-296, October.
    19. Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
      • Tobias Adrian & Markus K. Brunnermeier, 2011. "CoVaR," NBER Working Papers 17454, National Bureau of Economic Research, Inc.
    20. Scott A. Brave & Jose A. Lopez, 2019. "Calibrating Macroprudential Policy to Forecasts of Financial Stability," International Journal of Central Banking, International Journal of Central Banking, vol. 15(1), pages 1-59, March.
    21. Watson, Mark W. & Stock, James H., 2014. "Estimating turning points using large data sets," Scholarly Articles 33192198, Harvard University Department of Economics.
    22. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    23. Everett Grant & Julieta Yung, 2017. "The Double-Edged Sword of Global Integration: Robustness, Fragility & Contagion in the International Firm Network," Globalization Institute Working Papers 313, Federal Reserve Bank of Dallas.
    24. Flannery, Mark J, 1998. "Using Market Information in Prudential Bank Supervision: A Review of the U.S. Empirical Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 30(3), pages 273-305, August.
    25. Andreas Lehnert & Beverly Hirtle, 2015. "Supervisory Stress Tests," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 339-355, December.
    26. Bhanu Balasubramnian & Ajay Palvia, 2018. "Can short sellers inform bank supervision?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 53(1), pages 69-98, February.
    27. Pettway, Richard H & Sinkey, Joseph F, Jr, 1980. "Establishing On-Site Bank Examination Priorities: An Early-Warning System Using Accounting and Market Information," Journal of Finance, American Finance Association, vol. 35(1), pages 137-150, March.
    28. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2012. "Assessing the systemic risk of a heterogeneous portfolio of banks during the recent financial crisis," Journal of Financial Stability, Elsevier, vol. 8(3), pages 193-205.
    29. Krainer, John & Lopez, Jose A, 2004. "Incorporating Equity Market Information into Supervisory Monitoring Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(6), pages 1043-1067, December.
    30. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    31. John Krainer & Jose A. Lopez, 2008. "Using Securities Market Information for Bank Supervisory Monitoring," International Journal of Central Banking, International Journal of Central Banking, vol. 4(1), pages 125-164, March.
    32. 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.
    33. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    34. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    35. Morrison, Alan & Vasios, Michalis & Wilson, Mungo & Zikes, Filip, 2017. "Identifying contagion in a banking network," Bank of England working papers 642, Bank of England.
    36. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    37. John Krainer & Jose A. Lopez, 2003. "How might financial market information be used for supervisory purposes?," Economic Review, Federal Reserve Bank of San Francisco, pages 29-45.
    38. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
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    More about this item

    Keywords

    Financial stability; Bank supervision; Network centrality; Systemic connectedness;
    All these keywords.

    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
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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