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  • Approximating the Effects of Parameter Uncertainty on Value at Risk Estimates
    (15), and (16), we write (6) as ∂VaR(θS) ∂θSi ≈ (17) − h f S˜r∗ [ r∗∑ j=o T−1 ({ P ′N ( T (fX) ) ... offset by the marginal cost of further reduction. 17 7 Conclusion We derived a first order approximation ...

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    • Authors: Jacques Rioux, Steven Major, Donald Erdman
    • Date: Nov 2010
    • Competency: External Forces & Industry Knowledge>Actuarial methods in business operations
    • Topics: Finance & Investments>Risk measurement - Finance & Investments; Finance & Investments>Value at risk - Finance & Investments
  • Toward a Unified Approach to Fitting Loss Models
    following cubic functions provide a good approximation. 17 10% : 0.9289p3 − 2.6822p2 + 2.5761p+ 0.4011 5% ... simplicity and favors the lognormal model. Figures 17 and 18 below provide p−p plots for the two additional ...

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    • Authors: Stuart Klugman, Jacques Rioux
    • Date: Jan 2003
    • Competency: Results-Oriented Solutions; Technical Skills & Analytical Problem Solving
    • Publication Name: Actuarial Research Clearing House
    • Topics: Modeling & Statistical Methods