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Publisher: SFB 649, Economic Risk Berlin
Types: Article
Subjects: C63, C21, Lasso, G01, Wirtschaft, Systemic Risk, Statistik, C51, G32, Quantile Single-Index Regression, G38, G18, Value at Risk, CoVaR, HB, Systemic Risk, Systemic Risk Network, Generalized Quantile, Quantile Single-Index Regression, Value at Risk, CoVaR, Lasso, Systemic Risk Network, Generalized Quantile
jel: jel:G32, jel:C51, jel:C21, jel:G01, jel:C63, jel:G38, jel:G18
ddc: ddc:330, ddc:310
CoVaR is a measure for systemic risk of the networked financial system conditional on institutions being under distress. The analysis of systemic risk is the focus of recent econometric analyses and uses tail event and network based techniques. Here, in this paper we bring tail event and network dynamics together into one context. In order to pursue such joint efforts, we propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spillover effects in a high dimensional framework. The systemically important institutions are identified conditional to their interconnectedness structure. Methodologically, a variable selection technique in a time series setting is applied in the context of a single-index model for a generalized quantile regression framework. We could thus include more financial institutions into the analysis to measure their tail event interdependencies and, at the same time, be sensitive to non-linear relationships between them. Network analysis, its behaviour and dynamics, allows us to characterize the role of each financial industry group in 2007–2012: the depositories received and transmitted more risk among other groups, the insurers were less affected by the financial crisis. The proposed TENET - Tail Event driven NETwork technique allows us to rank the Systemic Risk Receivers and Systemic Risk Emitters in the US financial market.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Acharya, V., Engle, R., and Richardson, M. (2012). Capital shortfall: A new approach to ranking and regulating systemic risks. The American Economic Review, 102(3):59{64
    • Adrian, T. and Brunnermeier, M. K. (2011). CoVaR. Sta reports 348, Federal Reserve Bank of New York.
    • Beale, N., Rand, D. G., Battey, H., Croxson, K., May, R. M., and Nowak, M. A. (2011). Individual versus systemic risk and the regulator's dilemma. Proceedings of the National Academy of Sciences, 108(31):12647{12652
    • Belloni, A. and Chernozhukov, V. (2011). L1-penalized quantile regression in highdimensional sparse models. The Annals of Statistics, 39(1):82{130
    • Berkowitz, J., Christo ersen, P., and Pelletier, D. (2011). Evaluating value-at-risk models with desk-level data. Management Science, 57(12):2213{2227.
    • Billio, M., Getmansky, M., Lo, A. W., and Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the nance and insurance sectors. Journal of Financial Economics, 104(3):535{559
    • Bisias, D., Flood, M., Lo, A. W., and Valavanis, S. (2012). A survey of systemic risk analytics. Annu. Rev. Financ. Econ., 4(1):255{296
    • Chao, S.-K., Hardle, W. K., and Wang, W. (2015). Quantile regression in risk calibration. Handbook of Financial Econometric and Statistics, pages 1467{1489.
    • Diebold, F. X. and Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of nancial rms. Journal of Econometrics, 182:119{134.
    • Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456):1348{1360
    • Fan, Y., Hardle, W. K., Wang, W., and Zhu, L. (2013). Composite quantile regression for the single-index model. SFB 649 Discussion Paper 2013-010, Humbold-Universitat zu Berlin. Revised and resubmitted to Journal of Business and Economic Statistics.
    • Franke, J., Mwita, P., and Wang, W. (2014). Nonparametric estimates for conditional quantiles of time series. AStA Advances in Statistical Analysis, pages 1{24.
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    • Hardle, W. K., Muller, M., Sperlich, S., and Werwatz, A. (2004). Nonparametric and semiparametric models. Springer.
    • Hautsch, N., Schaumburg, J., and Schienle, M. (2015). Financial network systemic risk contributions. Review of Finance, 19(2):685{738.
    • Huang, X., Zhou, H., and Zhu, H. (2009). A framework for assessing the systemic risk of major nancial institutions. Journal of Banking and Finance, 33(11):2036{2049.
    • Koenker, R., Ng, P., and Portnoy, S. (1994). Quantile smoothing splines. Biometrika, 81(4):673{680
    • Li, Y. and Zhu, J. (2008). L1-norm quantile regression. Journal of Computational and Graphical Statistics, 17(1).
    • Minsky, H. P. (1977). A theory of systemic fragility. Financial crises: Institutions and markets in a fragile environment, pages 138{152.
    • Rodriguez-Moreno, M. and Pen~a, J. I. (2013). Systemic risk measures: The simpler the better? Journal of Banking and Finance, 37(6):1817{1831
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

    Title Trust
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