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A general approach to integrated risk management with skewed, fat-tailed risks

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  • Joshua V. Rosenberg
  • Til Schuermann
Abstract
The goal of integrated risk management in a financial institution is to measure and manage risk and capital across a range of diverse business activities. This requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. In this paper, we use the method of copulas to construct the joint risk distribution for a typical large, internationally active bank. This technique allows us to incorporate realistic marginal distributions that capture some of the essential empirical features of these risks-such as skewness and fat tails-while allowing for a rich dependence structure. ; We explore the impact of business mix and inter-risk correlations on total risk, whether measured by value at risk or expected shortfall. We find that given a risk type, total risk is more sensitive to differences in business mix or risk weights than it is to differences in inter-risk correlations. A complex relationship between volatility and fat tails exists in determining the total risk: whether they offset or reinforce each other will depend on the setting. The choice of copula (normal versus student-t), which determines the level of tail dependence, has a more modest effect on risk. We then compare the copula-based method with several conventional approaches to computing risk, each of which may be thought of as an approximation. One easily implemented approximation, which uses empirical correlations and quantile estimates, tracks the copula approach surprisingly well. In contrast, the additive approximation, which assumes no diversification benefit, typically overestimates risk by about 30 to 40 percent.

Suggested Citation

  • Joshua V. Rosenberg & Til Schuermann, 2004. "A general approach to integrated risk management with skewed, fat-tailed risks," Staff Reports 185, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:185
    Note: For a published version of this report, see Joshua V. Rosenberg and Til Schuermann, "A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risks," Journal of Financial Economics 79, no. 3 (March 2006): 569-614.
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    References listed on IDEAS

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    1. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    2. Gourieroux, C. & Laurent, J. P. & Scaillet, O., 2000. "Sensitivity analysis of Values at Risk," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 225-245, November.
    3. Jean-David FERMANIAN & Olivier SCAILLET, 2003. "Nonparametric Estimation of Copulas for Time Series," FAME Research Paper Series rp57, International Center for Financial Asset Management and Engineering.
    4. Beverly Hirtle, 2003. "What market risk capital reporting tells us about bank risk," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 37-54.
    5. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
    6. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    7. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
    8. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 2002. "Portfolio Value‐at‐Risk with Heavy‐Tailed Risk Factors," Mathematical Finance, Wiley Blackwell, vol. 12(3), pages 239-269, July.
    9. Joshua V. Rosenberg, 2003. "Nonparametric pricing of multivariate contingent claims," Staff Reports 162, Federal Reserve Bank of New York.
    10. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    11. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    12. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
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    More about this item

    Keywords

    copula; risk diversification; operational risk; market risk; credit risks;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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