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6 - Statistical Assessments of Systemic Risk Measures

from PART II - STATISTICS AND SYSTEMIC RISK

Published online by Cambridge University Press:  05 June 2013

Carole Bernard
Affiliation:
University of aterloo
Eike Christian Brechmann
Affiliation:
Technische Universität München
Claudia Czado
Affiliation:
Universität München
Jean-Pierre Fouque
Affiliation:
University of California, Santa Barbara
Joseph A. Langsam
Affiliation:
University of Maryland, College Park
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Summary

Abstract In this chapter, we review existing statistical measures for systemic risk and discuss their strengths and weaknesses. Among them we discuss the Conditional Value-at-Risk (CoVaR) introduced by Adrian and Brunnermeier (2010) and the Systemic Expected Shortfall (SES) of Acharya, Pedersen, Philippon and Richardson (2011). As systemic risk is highly related to financial contagion, we will explain the drawbacks and advantages of looking at “coexceedances” (simultaneous extreme events) or at the local changes in “correlation” that have been proposed in the literature on financial contagion (Bae, Karolyi and Stulz (2003), Baig and Goldfajn (1999) and Forbes and Rigobon (2002)).

Introduction and background on systemic risk

During the financial crisis of 2007–2009, worldwide taxpayers had to bailout many financial institutions. Governments are now trying to understand why the regulation failed, why capital requirements were not enough and how a guaranty fund should be built to address the next financial crisis. To implement such a fund, one needs to understand the risk that each institution represents to the financial system and why regulatory capital requirements were not enough. In the financial and insurance industry, capital requirements have the following common properties. First, they depend solely on the distribution of the institution's risk and not on the outcomes in the different states of the world. Second, capital requirements and marginal calculations treat each institution in isolation. An important element is missing in the above assessment of risk: it is the dependency between the individual institution and the economy or the financial system. The regulation should “be regulating each bank as a function of both its joint (correlated) risk with other banks as well as its individual (bank-specific) risk” (Acharya (2009)).

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Publisher: Cambridge University Press
Print publication year: 2013

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References

Aas, K., C., Czado, A., Frigessi, and H., Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2) 182–198.Google Scholar
Acharya, V. (2009). A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability 5 224-255.CrossRefGoogle Scholar
Acharya, V., L., Pedersen, T., Philippon, and M., Richardson (2011). Measuring systemic risk. AFA 2011 Denver Meetings Paper. Available athttp://dx.doi.org/10.2139/ssrn.1573171.CrossRef
Adrian, T., and M., Brunnermeier (2010). CoVaR. FRB of New York Staff Report No. 348. Available at SSRN: http://ssrn.com/abstract=1269446.
Ang, A., and J., Chen (2002). Asymmetric correlations of equity portfolios. Journal of Financial Economics 63 443-494.CrossRefGoogle Scholar
Bae, K.-H., G. A., Karolyi, and R. M., Stulz (2003). A new approach to measuring financial contagion. Review of Financial Studies 16 (3) 717–763.CrossRefGoogle Scholar
Baig, T., and I., Goldfajn (1999). Financial market contagion in the Asian crisis. working paper, International Monetary Fund, Washington DC.
Beine, M., A., Cosma, and R., Vermeulen (2010). The Dark side of global integration: increasing tail dependence. Journal of Banking and Finance 34 184-192.CrossRefGoogle Scholar
Billio, M., M., Getmansky, A., Lo, and L., Pelizzon (2010). Econometric measures of systemic risk in the finance and insurance sectors. NBER Working Paper 16223.
Bradley, B., and M., Taqqu (2004). Framework for analyzing spatial contagion between financial markets. Finance Letters 2 (6) 8–15.Google Scholar
Brechmann, E. (2010). Truncated and simplified regular vines and their applications. Master's thesis, Technische Universitat Munchen.
Brownlees, C., and R., Engle (2011). Volatility, Correlation and tails for systemic risk measurement. Working Paper Available at SSRN: http://ssrn.com/abstract= 1611229.
Campbell, R., C., Forbes, K., Koedijk, and P., Kofman (2008). Increasing correlations or just fat tails?Journal of Empirical Finance 15 287-309.CrossRefGoogle Scholar
Embrechts, P., H., Hoing, and A., Juri (2003). Using copulae to bound the Value-at-Risk for functions of dependent risks. Finance & Stochastics 7 145-167.CrossRefGoogle Scholar
Fernandez, C., and M. F., Steel (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association 93 359-371.Google Scholar
Forbes, K. J., and R., Rigobon (2002). No contagion, only interdependence: measuring stock market comovements. Journal of Finance 57 (5) 2223–2261.CrossRefGoogle Scholar
Hautsch, N., J., Schaumburg, and M., Schienle (2011). Quantifying time-varying marginal systemic risk contributions. Working Paper, Humboldt-Universitat zu Berlin.Google Scholar
Joe, H. (1997): Multivariate Models andDependence Concepts. Chapman & Hall, London.CrossRefGoogle Scholar
Juri, A., and M., Wuhtrich (2003). Tail dependence from a distributional point of view. Extremes 3 213-246.Google Scholar
Longin, F., and B., Solnik (2001). Extreme correlation of international equity markets. Journal of Finance 56 (2) 649–676.CrossRefGoogle Scholar
McNeil, A. J., R., Frey, and P., Embrechts (2005). Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton University Press.Google Scholar
Nelsen, R. B. (2006). An Introduction to Copulas, 2nd ed. Springer, Berlin.Google Scholar
Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de L'Université de Paris 8 229-231.Google Scholar

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