Economics - Research Publications

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Author: TAYLOR, GREGORY × Start date: 2001 × Subject: loss reserving ×

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    Loss reserving: past, present and future
    TAYLOR, GC ; MCGUIRE, G ; GREENFIELD, A (Centre for Actuarial Studies, The University of Melbourne, 2003)
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    Loss reserving with GLMs : a case study
    Taylor, Greg ; McGuire, Gráinne ( 2004-05)
    This paper provides a case study in the application of generalised linearmodels (“GLMs”) to loss reserving. The study is motivated by approachingthe exercise from the viewpoint of an actuary with a predisposition to theapplication of the chain ladder (“CL”).The data set under study is seen to violate the conditions for application of theCL in a number of ways. The difficulties of adjusting the CL to allow forthese features of the data are noted (Sections 3).Regression, and particularly GLM regression, is introduced as a structured andrigorous form of data analysis. This enables the investigation and modellingof a number of complex features of the data responsible for the violation of the CL conditions. These include superimposed inflation and changes in the rules governing the payment of claims (Sections 4 to 7).The development of the analysis is traced in some detail, as is the production of a range of diagnostics and tests used to compare candidate models and validate the final one.The benefits of this approach are discussed in Section 8.
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    Synchronous bootstrapping of seemingly unrelated regressions
    Taylor, Greg ; McGuire, Gráinne ( 2005-08)
    Consider the seemingly unrelated regression framework, in which regression models are applied to a number of data sets, with stochastic dependencies between them. The regression models are not restricted to general linear models (e.g. GLMs). Forecasts are required, with estimates of prediction errors that account for the dependencies between data sets. Bootstrapping is used to estimate prediction errors. Specialised forms of bootstrapping that capture the dependencies are constructed.Insurance and banking applications are mentioned. The former is investigated with numerical examples. The specific context is insurance loss reserving under the requirement that the entire distribution of loss reserve be estimated, where this reserve is aggregated across a number of stochastically dependent lines of business.