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  • Simplified Cash Flow Testing of Traditional Participating Whole Life Insurance
    Committee Effective August 11, 1995 65 Table of Contents Introduction a. An Overviewofthe ... of course, [ want to thank my supervisor, Frank S. Irish, for his continua] support and guidance tl~ough ...

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    • Authors: Dorothy Andrews
    • Date: Jan 1996
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Life Insurance>Reserves - Life Insurance; Life Insurance>Whole life; Modeling & Statistical Methods>Stochastic models
  • Applications of Multidimensional Contingency Tables to the Analysis of Termination Counts In Disability Income Claim Data
    Applications of Multidimensional Contingency Tables to the Analysis of Termination Counts ... In Disability Income Claim Data A contingency table is a set of counts or frequencies obtained by classifying ...

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    • Authors: Edward J Seligman
    • Date: Jan 1979
    • Competency: External Forces & Industry Knowledge>Actuarial methods in business operations
    • Publication Name: Actuarial Research Clearing House
    • Topics: Health & Disability>Disability insurance; Health & Disability>Disability tables; Modeling & Statistical Methods>Stochastic models
  • Development of a Simulation-based Model to Quantify the Degree of a Bank’s Liquidity Risk
    Simulation-based Model to Quantify the Degree of a Bank’s Liquidity Risk 2011 Enterprise Risk Management Symposium ... measurement framework using Black-Scholes and Merton’s asset-based models to measure liquidity risk. Banking ...

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    • Authors: Sadi Bin Asad Farooqui
    • Date: Mar 2011
    • Competency: External Forces & Industry Knowledge; Results-Oriented Solutions; Technical Skills & Analytical Problem Solving
    • Topics: Enterprise Risk Management; Global Perspectives; Modeling & Statistical Methods>Stochastic models; Public Policy
  • Inflated-Parameter Family of Generalized Power Series Distributions And Their Application In Analysis Of Overdispersed Insurance Data
    Inflated-Parameter Family of Generalized Power Series Distributions And Their Application In Analysis Of Overdispersed Insurance ... Assumptions;Computer science;Inflation;Risk theory; 805 1/1/2000 12:00:00 AM ...

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    • Authors: Nikolai Kolev, Leda Minkova, Plamen Neytchev
    • Date: Jan 2000
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Actuarial Profession>Professional development; Modeling & Statistical Methods>Stochastic models
  • On the Balducci Hypothesis
    On the Balducci Hypothesis This article investigates the simplicity of the Balducci hypothesis, and ... and constant force of mortality [1, 2]. Assumptions;Mortality rates=Mortality tables=Death rates ; ...

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    • Authors: Ho Kuen Ng
    • Date: Jan 1988
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Experience Studies & Data>Mortality; Modeling & Statistical Methods>Stochastic models
  • The Financial Implications of Finite Ruin Theory
    The Financial Implications of Finite Ruin Theory An insurance company starts with an initial ... stockholders. What remains is the following year’s surplus. The process continues. This paper describes ...

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    • Authors: Glenn Meyers
    • Date: Jan 1986
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Finance & Investments>Risk measurement - Finance & Investments; Modeling & Statistical Methods>Stochastic models
  • Risk Premiums and Their Applications
    Risk Premiums and Their Applications Je®rey S. Pai I. H. ASPER SCHOOL OF BUSINESS University of Manitoba ... ¦(n)(u) = E[f(X ¡ u)+gn]; u ¸ 0; n = 1; 2; ¢ ¢ ¢ ; (1) where (x¡ u)+ = 8><>: 0; for x · u;x¡ u; for ...

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    • Authors: Jeffrey S Pai
    • Date: Jan 2001
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Finance & Investments>Risk measurement - Finance & Investments; Modeling & Statistical Methods>Stochastic models
  • Bayesian Reserving Models Inspired by Chain Ladder Methods and Implemented Using WinBUGS
    original methodology back to Harnek (1966). Taylor (2000, page 26) describes chain ladder models for reserving ... to date to its predicted ultimate value.’ Taylor (2000, Chapter 3) provides a detailed overview of traditional ...

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    • Authors: David Scollnik
    • Date: Sep 2008
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Topics: Economics>Financial economics; Modeling & Statistical Methods>Bayesian methods; Modeling & Statistical Methods>Stochastic models
  • Asymptotics In The Subexponential Case
    Asymptotics In The Subexponential Case This is a summary of the presentation given during the ARC Conference. Its purpose was to give ... the actuarial literature. Risk theory; 800 1/1/2000 12:00:00 AM ...

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    • Authors: DIEGO HERNANDEZRANGEL
    • Date: Jan 2000
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Finance & Investments>Risk measurement - Finance & Investments; Modeling & Statistical Methods>Stochastic models
  • Martingales and Ruin Probability
    and then use it to give a short proof of Lundb(ng s inequality. Theorem 1.1. Let X = (X,,)n~r be a sub-martingale ... E(X~ +) < E(iXN]). (1) A.P( ,nax X,, > A) < E(XN : u<,<N o _ < , , < N - - - - _ _ - - - - - - ...

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    • Authors: Gordon E Willmot, Hailiang Yang
    • Date: Jan 1996
    • Competency: External Forces & Industry Knowledge>Actuarial theory in business context
    • Publication Name: Actuarial Research Clearing House
    • Topics: Finance & Investments>Risk measurement - Finance & Investments; Modeling & Statistical Methods>Stochastic models