Skip to main content
Article thumbnail
Location of Repository

Banking stability measures

By Miguel A. Segoviano and Charles Goodhart

Abstract

The recent crisis underlined that proper estimation of distress-dependence amongst banks in a global system is essential for financial stability assessment. We present a set of banking stability measures embedding banks’ linear (correlation) and nonlinear distress-dependence, and their changes through the economic cycle, thereby allowing analysis of stability from three complementary perspectives: common distress in the system, distress between specific banks, and cascade effects associated with a specific bank. Our approach defines the banking system as a portfolio of banks and infers its multivariate density from which the proposed measures are estimated. These can be provided for developed and developing countries

Topics: HF Commerce, HG Finance
Publisher: Financial Markets Group, London School of Economics and Political Science
Year: 2009
OAI identifier: oai:eprints.lse.ac.uk:24416
Provided by: LSE Research Online

Suggested articles

Citations

  1. (1999). An Introduction to doi
  2. (1998). An Structural Approach for Portfolio Credit Risk vs.
  3. (2004). Bank Regulation and Macroeconomic Fluctuations,” Oxford Review of Economic Policy, doi
  4. (2006). Bank Restructuring and Resolution. Washington: International Monetary Fund. doi
  5. (2009). Banks’ Probability of Default: Which Methodology, When, and Why?” Forthcoming. International Monetary Fund Working Paper.
  6. (2004). Basel and Procyclicality: A Comparison of the Standardized and IRB Approaches to an Improved Credit Risk Method,” London School of Economics. Financial Markets Group Discussion Paper Series,
  7. (1999). Corollary: Let G be any joint distribution with continuous marginals
  8. (1998). Evaluating Density Forecasts with Applications to Financial Risk doi
  9. (1959). Information Theory and Statistics.” doi
  10. (2002). Internal Ratings, the Business Cycle and Capital Requirements: Some Evidence from an Emerging Market Economy,” BIS Working Paper 117. LSE/FMG discussion paper 428. doi
  11. (1999). Multivariate Density Forecast Evaluation and Calibration doi
  12. (2006). Portfolio Credit risk and Macroeconomic Shocks: Applications to Stress Testing under Data Restricted Environments,” International Monetary Fund Working Paper 06/283. doi
  13. (2000). Portfolio Value-at-Risk with Heavy-Tailed Risk Factors,” doi
  14. (2006). Searching for a Metric for Financial Stability,” London
  15. (1999). Sklar’s Theorem Sklar’s Theorem is used in all applications of copulas. Let G be a joint distribution function with marginals F and H. Then there exists a copula C such that for all x, y in ,
  16. (2002). Statistical Inference,
  17. (1992). Statistics of Bivariate Extreme Values,” Tinbergen Institute Research Series, Ph.D. Thesis No 22.
  18. (2001). Systemic Risk: A Survey,” doi
  19. (1997). Tail Index and Quantile Estimation with Very High Frequency Data,” doi
  20. (2006). The Consistent Information Multivariate Density Optimizing Methodology,” Financial Markets Group, London School of Economics Discussion Paper 557.
  21. (2005). The Simple Economics of Bank Fragility,” doi
  22. (1990). Under the PIT, two new variables are defined as
  23. y q x y dxdy doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.