Economics - Research Publications
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ItemHow to proxy the unmodellable: Analysing granular insurance claims in the presence of unobservable or complex drivers(Institute of Actuaries, Australia, 2018)The estimation of claim and premium liabilities is a key component of an actuary's role and plays a vital part of any insurance company’s operations. In practice, such calculations are complicated by the stochastic nature of the claims process as well as the impracticality of capturing all relevant and material drivers of the observed claims data. In the past, computational limitations have promoted the prevalence of simplified (but possibly sub-optimal) aggregate methodologies. However, in light of modern advances in processing power, it is viable to increase the granularity at which we analyse insurance data sets so that potentially useful information is not discarded. By utilising more granular and detailed data (that is usually readily available to insurers), model predictions may become more accurate and precise. Unfortunately, detailed analysis of large insurance data sets in this manner poses some unique challenges. Firstly, there is no standard framework to which practitioners can refer and it can be challenging to tractably integrate all modelled components into one comprehensive model. Secondly, analysis at greater granularity or level of detail requires more intense levels of scrutiny as complex trends and drivers that were previously masked by aggregation and discretisation assumptions may emerge. This is particularly an issue with claim drivers that are either unobservable to the modeller or very difficult/expensive to model. Finally, computation times are a material concern when processing such large volumes of data as model outputs need to be obtained in reasonable time-frames. Our proposed methodology overcomes the above problems by using a Markov-modulated non-homogeneous Poisson process framework. This extends the standard Poisson model by allowing for over-dispersion to be captured in an interpretable, structural manner. The approach implements a flexible exposure measure to explicitly allow for known/modelled claim drivers while the hidden component of the Hidden Markov model captures the impact of unobservable or practicably non-modellable information. Computational developments are made to drastically reduce calibration times. Theoretical findings are illustrated and validated in an empirical case study using Australian general insurance data in order to highlight the benefits of the proposed approach.
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ItemOn the Impact, Detection and Treatment of Outliers in Robust Loss Reserving(Actuaries Institute, 2016)The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. For instance, it has been shown that the popular chain-ladder reserving approach is at significant risk to such aberrant observations in that reserve estimates can be significantly shifted in the presence of even one outlier. In this paper we firstly investigate the sensitivity of reserves and mean squared errors of prediction under Mack's Model. This is done through the derivation of impact functions which are calculated by taking the first derivative of the relevant statistic of interest with respect to an observation. We also provide and discuss the impact functions for quantiles when total reserves are assumed to be lognormally distributed. Additionally, comparisons are made between the impact functions for individual accident year reserves under Mack's Model and the Bornhuetter-Ferguson methodology. It is shown that the impact of incremental claims on these statistics of interest varies widely throughout a loss triangle and is heavily dependent on other cells in the triangle. We then put forward two alternative robust bivariate chain-ladder techniques (Verdonck and VanWouwe, 2011) based on Adjusted-Outlyingness (Hubert and Van der Veeken, 2008) and bagdistance (Hubert et al., 2016). These techniques provide a measure of outlyingness that is unique to each individual observation rather than largely relying on graphical representations as is done under the existing bagplot methodology. Furthermore the Adjusted Outlyingness approach explicitly incorporates a robust measure of skewness into the analysis whereas the bagplot captures the shape of the data only through a measure of rank. Results are illustrated on two sets of real bivariate data from general insurers.
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ItemMicroblogging and Life Changes: An Ethnographic and Statistical Analysis of Young Adults(The International Academic Forum (IAFOR), 2014)Microblogging has revolutionized people's interaction on the web. This paper investigates the changes in the microblogging practices of young adults after they have experienced life changing events associated with studying and working overseas. To test for the presence of significant changes in microblogging behaviour the empirical analysis focusses on young Chinese adults who have moved to Australia to study and/or work. The data consists of a three-tier approach, with the first tier being based on questionnaires; the second tier consists of formal in-depth interviews; while the third tier involves an ethnographic analysis of online and offline participant behaviour as well as information collected from two focus groups. The behavioural changes of the participants are analysed using a range of statistical models which take into account microblogging practices relating to social media platform choices, behavioural strategies and frequency. Formally this involves using panel ordered probit models to identify potential significant changes in social media practices. In specifying the empirical models, key demographic attributes characterizing the participants are also incorporated into the analysis, including gender, age, location, duration, education, enrolment status and work status. The empirical results reveal evidence of significant changes in key social media practices of Chinese young adults in moving from China to Australia. Keywords: social media, study overseas, work overseas, three-tier approach. Official conference proceedings published by IAFOR are available at: http://iafor.org/issn-2186-5906-the-asian-conference-on-media-mass-communication-2014-official-conference-proceedings/