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Operational risk with excel and VBA: applied statistical methods for risk management

By: Da Costa Lewis, NigelMaterial type: TextTextOriginal language: Spanish Publisher: New Jersey ; John Wiley ; 2004Description: 267 pISBN: 0-471-47887-3Subject(s): Gestión de riesgos | Modelos estadísticos | Modelos matemáticosLOC classification: HD/61/L49/2004
Contents:
Contiene: 1. Introduction to operational risk management and modeling.- 2. Random variables, risk indicators, and probability.- 3. Expectation, covariance, variance and correlation.- 4. Modelling central tendency and variability of operational risk indicators.- 5. Measuring skew and fat tails of operational risk indicators.- 6. Statistical testing of operational risk parameters.- 7. Severity of loss probability models.- 8. Frequency of loss probability models.- 9. Modelling aggregate loss distributions.- 10. The law of significant digits and fraud risk identification.- 11. Correlation and dependence.- 12. Linear regression in operational risk management.- 13. Logistic regression in operational risk management.- 14. Mixed dependent variable modeling.- 15. Validating operational risk proxies using surrogate endpoints.- 16. Introduction to extreme value theory.- 17. Managing operational risk with bayesian networks.- 18. Epilogue.- 19. Statistical tables.
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Libros Libros Biblioteca de la Superintendencia del Mercado de Valores - SMV
HD/61/L49/2004 (Browse shelf) Available 00008099

Contiene: 1. Introduction to operational risk management and modeling.- 2. Random variables, risk indicators, and probability.- 3. Expectation, covariance, variance and correlation.- 4. Modelling central tendency and variability of operational risk indicators.- 5. Measuring skew and fat tails of operational risk indicators.- 6. Statistical testing of operational risk parameters.- 7. Severity of loss probability models.- 8. Frequency of loss probability models.- 9. Modelling aggregate loss distributions.- 10. The law of significant digits and fraud risk identification.- 11. Correlation and dependence.- 12. Linear regression in operational risk management.- 13. Logistic regression in operational risk management.- 14. Mixed dependent variable modeling.- 15. Validating operational risk proxies using surrogate endpoints.- 16. Introduction to extreme value theory.- 17. Managing operational risk with bayesian networks.- 18. Epilogue.- 19. Statistical tables.

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