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Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

Published online by Cambridge University Press:  21 May 2018

Marco Valerio Geraci  and
Jean-Yves Gnabo
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Abstract

We propose a market-based framework that exploits time-varying parameter vector autoregressions to estimate the dynamic network of financial spillover effects. We apply it to financials in the Standard & Poor’s 500 index and estimate interconnectedness at the sectoral and institutional levels. At the sectoral level, we uncover two main events in terms of interconnectedness: the Long-Term Capital Management crisis and the 2008 financial crisis. After these crisis events, we find a gradual decrease in interconnectedness, not observable using the classical rolling-window approach. At the institutional level, our framework delivers more stable interconnectedness rankings than other comparable market-based measures.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 53 , Issue 3 , June 2018 , pp. 1371 - 1390
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

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Footnotes

1

We thank Tobias Adrian (the referee), Matteo Barigozzi, Christiane Baumeister, Sophie Béreau, Oscar Bernal, Monica Billio, Christian Brownlees, Alain de Combrugghe, Frank Diebold, Michele Lenza, Matteo Luciani, Paul Malatesta (the editor), David Veredas, and Kamil Yılmaz and conference and seminar participants at the 2016 French Finance Association Conference, the 2016 Conference on Financial Risk and Network Theory, the 2016 European Economic Association Meeting, Fordham University, Université Libre de Bruxelles, University of Mannheim, and Université de Namur for valuable comments. We also gratefully acknowledge financial support from the Communauté Française de Belgique. We are responsible for all remaining errors and omissions.

References

Acharya, V. V.; Engle, R. F.; and Richardson, M.. “Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risk.” American Economic Review, 102 (2012), 59–64.Google Scholar
Acharya, V. V.; Pedersen, L. H.; Philippon, T.; and Richardson, M.. “Measuring Systemic Risk.” Review of Financial Studies, 30 (2017), 2–47.CrossRefGoogle Scholar
Adams, Z.; Füss, R.; and Gropp, R.. “Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk Approach.” Journal of Financial and Quantitative Analysis, 49 (2014), 575–598.CrossRefGoogle Scholar
Adrian, T., and Brunnermeier, M. K.. “CoVaR.” American Economic Review, 106 (2016), 1705–1741.Google Scholar
Barigozzi, M., and Brownlees, C.. “NETS: Network Estimation for Time Series.” ECARES Working Paper, Université Libre de Bruxelles (2017).Google Scholar
Barigozzi, M., and Hallin, M.. “A Network Analysis of the Volatility of High Dimensional Financial Series.” Journal of the Royal Statistical Society, Series C: Applied Statistics, 66 (2017), 581–605.Google Scholar
Baumeister, C., and Benati, L.. “Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound.” International Journal of Central Banking, 9 (2013), 165–212.Google Scholar
Bernardi, M.; Gayraud, G.; and Petrella, L.. “Bayesian Tail Risk Interdependence Using Quantile Regression.” Bayesian Analysis, 10 (2015), 553–603.Google Scholar
Billio, M.; Getmansky, M.; Lo, A. W.; and Pelizzon, L.. “Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors.” Journal of Financial Economics, 104 (2012), 535–559.Google Scholar
Brownlees, A. C., and Engle, R. F.. “SRisk: A Conditional Capital Shortfall Measure of Systemic Risk.” Review of Financial Studies, 30 (2017), 48–79.CrossRefGoogle Scholar
Ciccarelli, M., and Rebucci, A.. “Measuring Contagion and Interdependence with a Bayesian Time-Varying Coefficient Model: An Application to the Chilean FX Market during the Argentine Crisis.” Journal of Financial Econometrics, 5 (2007), 285–320.CrossRefGoogle Scholar
Clark, T. E., and McCracken, M. W.. “Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts.” International Economic Review, 50 (2009), 363–395.CrossRefGoogle Scholar
Cogley, T., and Sargent, T.. “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US.” Review of Economic Dynamics, 8 (2005), 262–302.Google Scholar
Committee on Oversight and Government Reform. “Causes and Effects of the Lehman Brothers Bankruptcy: Hearing before the Committee on Oversight and Government Reform.” Available at https://www.gpo.gov/fdsys/pkg/CHRG-110hhrg55766/html/CHRG-110hhrg55766.htm(2008).Google Scholar
Daníelsson, J.; James, K.; Valenzuela, M.; and Zer, I.. “Model Risk of Risk Models.” Journal of Financial Stability, 23 (2016), 79–91.Google Scholar
Diebold, F. X., and Yılmaz, K.. “Measuring Financial Asset Return and Volatility Spillovers, with Application to the Global Equity Markets.” Economic Journal, 119 (2009), 158–171.Google Scholar
Diebold, F. X., and Yılmaz, K.. “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms.” Journal of Econometrics, 182 (2014), 119–134.CrossRefGoogle Scholar
Dungey, M.; Luciani, M.; and Veredas, D.. “Systemic Risk in the US: Interconnectedness as a Circuit Breaker.” Economic Modelling, 71 (2018), 305–315.Google Scholar
Engle, R. F.“Dynamic Conditional Beta.” Journal of Financial Econometrics, 14 (2016), 643–667.Google Scholar
Engle, R. F.; Jondeau, E.; and Rockinger, M.. “Systemic Risk in Europe.” Review of Finance, 19 (2015), 145–190.Google Scholar
Financial Stability Board. “Policy Measures to Address Systemically Important Financial Institutions.” Available at http://www.fsb.org/2011/11/r_111104bb/(2011).Google Scholar
Hautsch, N.; Schaumburg, J.; and Schienle, M.. “Forecasting Systemic Impact in Financial Networks.” International Journal of Forecasting, 30 (2014), 781–794.Google Scholar
Hautsch, N.; Schaumburg, J.; and Schienle, M.. “Financial Network Systemic Risk Contributions.” Review of Finance, 19 (2015), 685–738.Google Scholar
Kim, C.-J., and Nelson, C. R.. State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT Press (1999).Google Scholar
Koop, G.; Leon-Gonzalez, R.; and Strachan, R. W.. “Dynamic Probabilities of Restrictions in State Space Models: An Application to the Phillips Curve.” Journal of Business and Economic Statistics, 28 (2010), 370–379.Google Scholar
McDonough, M. J.“Statement by William J. McDonough, President Federal Reserve Bank of New York before the Committee on Banking and Financial Services U.S. House of Representatives.” Available at https://www.newyorkfed.org/newsevents/speeches/1998/mcd981001 (1998).Google Scholar
Nakajima, J., and Omori, Y.. “Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student’s t-Distribution.” Computational Statistics and Data Analysis, 56 (2012), 3690–3704.Google Scholar
Nucera, F.; Schwaab, B.; Koopman, S. J.; and Lucas, A.. “The Information in Systemic Risk Rankings.” Journal of Empirical Finance, 38 (2016), 461–475.Google Scholar
Primiceri, G.“Time Varying Structural Vector Autoregressions and Monetary Policy.” Review of Economic Studies, 72 (2005), 821–852.Google Scholar
Scott, H. S.“Interconnectedness and Contagion.” Tech. Rep., Committee on Capital Markets Regulation (2012).Google Scholar
White, H.; Kim, T.-H.; and Manganelli, S.. “VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles.” Journal of Econometrics, 187 (2015), 169–188.Google Scholar
Yu, K., and Moyeed, R. A.. “Bayesian Quantile Regression.” Statistics and Probability Letters, 54 (2001), 437–447.Google Scholar
Zivot, E., and Wang, J.. “Rolling Analysis of Time Series.” Modeling Financial Time Series with S-PLUS, 2nd ed. New York: Springer (2006).Google Scholar
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