Economics - Research Publications

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    Financial metrics for comparing Australian retirement villages
    Kyng, T ; Pitt, D ; Purcal, S ; Zhang, J (WILEY, 2021-12)
    Abstract Retirement village contracts are complex, blending together financial options on real estate, life annuities and life insurance. We analyse the structure of the cash flows involved in a retirement village contract and distil the cost components into an equivalent monthly comparison rent. In general, we observe lower monthly rents when the maintenance fees and deferred management fees are lower, when higher rates of capital gain are evident, and, importantly, when retirees reside in the retirement village for a longer period. Our analysis provides a framework to meaningfully compare the relative merits of the finances incorporated into retirement village contracts.
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    On the prediction of claim duration for income protection insurance policyholders
    Liu, Q ; Pitt, D ; Wu, X (Cambridge University Press, 2014-03)
    This paper explores how we can apply various modern data mining techniques to better understand Australian Income Protection Insurance (IPI). We provide a fast and objective method of scoring claims into different portfolios using available rating factors. Results from fitting several prediction models are compared based on not only the conventional loss prediction error function, but also a modified loss function. We demonstrate that the prediction power of all the data mining methods under consideration is clearly evident using a misclassification plot. We also point out that this predictability can be masked by looking at just the conventional prediction error function. We then suggest using the stepwise regression technique to reduce the number of variables used in the data mining methods. Apart from this variable selection method, we also look at principal components analysis to increase understanding of the rating factors that drive claim durations of insured lives. We also discuss and compare how different variable combining techniques can be used to weight available predicting variables. One interesting outcome we discover is that principal components analysis and the weighted combination prediction model together provide very consistent results on identifying the most significant variables for explaining claim durations.
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    Survival Analysis of Left Truncated Income Protection Insurance Data
    Liu, Q ; Pitt, D ; Wang, Y ; Wu, X (Walter de Gruyter GmbH, 2013)
    One of the main characteristics of Income Protection Insurance (IPI) claim duration data, which has not been considered in the actuarial literature on the topic, is left-truncation. Claimants that are observed are those whose sickness durations are longer than the deferred periods specified in the policies, and hence left-truncation exists in these data. This paper investigates a series of conditional mixture models when applying survival analysis to model sickness durations of IPI claimants, and examines the consequence of treating the IPI data with lengthy deferred periods as complete data and therefore ignoring the left-truncation by fitting the corresponding unconditional distributions. It also quantifies the extent of the bias in the resulting parameter estimates when ignoring the left-truncation in the data. Using the UK Continuous Mortality Investigation (CMI) sickness duration data, some well-fitting survival model results are estimated. It is demonstrated that ignoring the left-truncation in certain IPI data can lead to substantially different statistical estimates. We therefore suggest taking left-truncation into account by fitting conditional mixture distributions to IPI data. Furthermore, the best fitting model is extended by introducing a number of covariates into the conditional part to do regression analysis.
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    Modelling the Claim Duration of Income Protection Insurance Policyholders using Parametric Mixture Models
    Pitt, DGW (Cambridge University Press (CUP), 2007-03)
    ABSTRACT This paper considers the modelling of claim durations for existing claimants under income protection insurance policies. A claim is considered to be terminated when the claimant returns to work. Data used in the analysis were provided by the Life and Risk Committee of the Institute of Actuaries of Australia. Initial analysis of the data suggests the presence of a long-run probability, of the order of 7%, that a claimant will never return to work. This phenomenon suggests the use of mixed parametric regression models as a description of claim duration which include the prediction of a long-run probability of not returning to work. A series of such parametric mixture models was investigated, and it was found that the generalised F mixture distribution provided a good fit to the data and also highlighted the impact of a number of statistically significant predictors of claim duration.