IBM®
Skip to main content
    Country/region [change]    Terms of use
 
 
 
    Home    Products    Services & solutions    Support & downloads    My account    

IBM Journal of Research and Development
Business Optimization   Volume 51, Number 3/4, 2007
Table of contents: HTMLPDF This article: HTMLPDF   Copyright info

Modeling of risk losses using size-biased data - References

 by E. Yashchin
References

  1. Basel Committee on Banking Supervision, Convergence of Capital Measurement and Capital Standards, Bank for International Settlements, Basel, Switzerland, 2006.
  2. M. G. Cruz, Modeling, Measuring and Hedging Operational Risk, Wiley, New York, 2002.
  3. D. N. Chorafas, Operational Risk Control with Basel II: Basic Principles and Capital Requirements, Elsevier, Amsterdam, The Netherlands, 2004.
  4. B. Engelmann and R. Rauhmeier, The Basel II Risk Parameters, Springer, Berlin–Heidelberg, Germany, 2006.
  5. H. H. Panjer, Operational Risks: Modeling Analytics, Wiley, New York, 2006.
  6. C. Alexander, “Rules and Models,” Risk 15, No. 1, 18–20 (2002).
  7. F. Cheng, D. Gamarnik, N. Jengte, W. Min, and B. Ramachandran, “Modeling Operational Risks in Business Processes,” Research Report RC-23672, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 2004.
  8. C. Supatgiat, C. Kenyon, and L. Heusler, “Cause-to-Effect Operational Risk Quantification and Management,” Risk Manage. 8, 16–42 (2006).
  9. P. Giudici and A. Bilotta, “Modeling Operational Losses: A Bayesian Approach,” Qual. & Reliabil. Eng. Intl. 20, 407–417 (2004).
  10. K. Adusei-Poku, “Operational Risk Management—Implementing a Bayesian Network for Foreign Exchange and Money Market Settlement,” Doctoral Thesis, University of Göttingen, Germany, 2005.
  11. A. Roehr, “Modeling Operational Losses,” Algo Res. Quart. 5, No. 2, 53–64 (2002).
  12. V. Chavez-Demoulin, P. Embrechts, and J. Neslehova, “Quantitative Models for Operational Risk: Extremes, Dependence and Aggregation,” J. Banking Finance 30, No. 10, 2635–2658 (2006).
  13. E. Yashchin, “Modeling of Risk Losses Based on Incomplete Data,” Research Report RC-23676, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 2005.
  14. J. R. Premister, U. Oktem, P. R. Kleindorfer, and H. Kunreuther, “Near-Miss Incident Management in the Chemical Process Industry,” Risk Anal. 23, No. 3, 445–459 (2006).
  15. G. Van den Brink, Operational Risk: The New Challenge for Banks, Palgrave, New York, 2002.
  16. D. K. Rosenberg and W. S. Overton, “Estimation of Animal Abundance When Capture Probabilities are Low and Heterogeneous,” J. Wildlife Manage. 59, 252–261 (1995).
  17. B. Littlewood, “Predicting Software Reliability,” Phil. Trans. Roy. Soc. Lond. A 327, 513–527 (1989).
  18. B. D. Olin and W. Q. Meeker, “Applications of Statistical Methods to Nondestructive Evaluation,” Technometrics 38, No. 2, 95–112 (1996).
  19. T. Alderweireld, J. Garcia, and L. Leonard, “A Practical Operational Risk Scenario Analysis Quantification,” Risk 19, No. 2, 93–95 (2006).
  20. E. L. Lehmann, Theory of Point Estimation, Wiley, New York, 1983.
  21. R. B. D'Agostino and M. A. Stephens, Goodness-of-Fit Techniques, Marcel Dekker, New York, 1986.
  22. J. Galambos, The Asymptotic Theory of Extreme Order Statistics, Robert E. Krieger Publishing Co., Malabar, FL, 1987.
  23. A. A. Balkema and L. de Haan, “Residual Lifetime at Great Age,” Ann. Probabil. 2, 792–804 (1972).
  24. J. Beirlant, Y. Goegebeur, J. Segers, and J. Teugels, Statistics of Extremes: Theory and Applications, Wiley, New York, 2004.
  25. K. Bocker, “Operational Risk: Analytical Results When High-Severity Losses Follow a Generalized Pareto Distribution (GPD)—A Note,” J. Risk 8, No. 4, 117–120 (2006).
  26. E. S. Pearson and H. O. Hartley, Biometrika Tables for Statisticians, Vol. 2, Biometrika Trust, London, U.K., 1976.
  27. A. C. Davison and D. V. Hinkley, Bootstrap Methods and Their Applications, Cambridge University Press, U.K., 1997.


    About IBM    Privacy    Contact