Creating portfolio-specific mortality tables: a case study

Richards, Stephen J.; Kaufhold, K.; Rosenbusch, S.

doi:10.1007/s13385-013-0076-6

Cited by 15 publications

(15 citation statements)

References 11 publications

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“…Remaining heterogeneity is then captured by observable risk factors in a Poisson GLM framework. Richards et al (2013) model the force of mortality using a time-varying version of the Makeham-Beard law and estimate the parameters on 5 years of historical portfolio data for individual lives. Their approach can, therefore, not be used when only aggregated portfolio data are available.…”

Section: General Population Mortality Let D Tmentioning

confidence: 99%

A Bayesian Joint Model for Population and Portfolio-Specific Mortality

2017

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Insurance companies and pension funds must value liabilities using mortality rates that are appropriate for their portfolio. These can only be estimated in a reliable way from a sufficiently large historical dataset for such portfolios, which is often not available. We overcome this problem by introducing a model to estimate portfolio-specific mortality simultaneously with population mortality. By using a Bayesian framework, we automatically generate the appropriate weighting for the limited statistical information in a given portfolio and the more extensive information that is available for the whole population. This allows us to separate parameter uncertainty from uncertainty due to the randomness in individual deaths for a given realization of mortality rates. When we apply our method to a dataset of assured lives in England and Wales, we find that different prior specifications for the portfolio-specific factors lead to significantly different posterior distributions for hazard rates. However, in short-term predictive distributions for future numbers of deaths, individual mortality risk turns out to be more important than parameter uncertainty in the portfolio-specific factors, both for large and for small portfolios.

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Section: General Population Mortality Let D Tmentioning

confidence: 99%

A Bayesian Joint Model for Population and Portfolio-Specific Mortality

2017

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“…For further details of actuarial applications of survival models to pensioner and annuitant mortality the reader can consult Richards et al . (2013).…”

Section: A Two-parameter Case: the Importance Of Acknowledging Correlmentioning

confidence: 99%

“…Mortality levels and rates of change by age are generally negatively correlated, as demonstrated later in Table 9, and this topic is explored in some detail for various risk factors in Richards et al . (2013). Appendix 5 considers how to restructure a model to reduce parameter correlation, but this only works for the very simplest models.…”

Section: A Two-parameter Case: the Importance Of Acknowledging Correlmentioning

confidence: 99%

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Mis-estimation risk: measurement and impact

Richards¹

2016

Br. Actuar. J.

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When deriving a demographic basis from experience data it is useful to know (i) what uncertainty surrounds that basis, and (ii) the financial impact of that uncertainty. Under the Solvency II regime in the EU, insurers must hold capital against a number of risks. One of these is mis-estimation risk, i.e. the uncertainty over the current rates of mortality and other biometric risks experienced by a portfolio. We propose a general method for assessing mis-estimation risk, and by way of illustration we look at how mis-estimation risk can be assessed for a portfolio of pensions in payment from a UK pension scheme. We find that the impact of mis-estimation risk varies according to the risk factors included in a model, and that the inclusion of some necessary risk factors increases the financial impact of mis-estimation risk. In particular, the inclusion of risk factors which improve the model’s fit and financial applicability can lead to an increase in the mis-estimation risk. We also find that a full-portfolio valuation is preferable to using model points when assessing mis-estimation risk.

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“…Historically, a shortage of expertise, resources, and data led individual pension schemes and insurance companies to outsource the creation of their mortality tables to third parties such as the Continuous Mortality Investigation (CMI) of the Institute and Faculty of Actuaries in the United Kingdom. 24 Recently, however, more companies are developing their own forecasting methodologies, albeit with cross-referencing with major industry players.…”

Section: Introductionmentioning

confidence: 99%

Longevity expectations in the pension fund, insurance, and employee benefits industries

Zhavoronkov¹

2015

PRBM

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Considerable progress has been made in many areas of biomedical science since the 1960s, suggesting likely increases in life expectancy and decreases in morbidity and mortality in the adult population. These changes may pose substantial risks to the pensions and benefits industries. While there is no significant statistical evidence demonstrating rapid decreases in mortality rates, there are conflicting opinions among demographers and biogerontologists on the biological limits of the human lifespan and trends in life expectancy. We administered a survey of the International Employee Benefits Association (IEBA), a large, international industry group. Industry professionals employed by consulting (35%), insurance (24%), pension (14%), and other (27%) companies responded to 32 questions. Respondents showed reasonably conservative views on the future of longevity and retirement, including that for women. The respondents formed their personal longevity expectations based on their family history and, to a lesser degree, on the actuarial life tables. Most of the sample expressed no desire to life past age 100 years, even if the enabling technologies required to maintain a healthy youthful state were available, and only a few respondents in the sample expressed a desire to live for the maximum period (at least) offered by the survey question. The majority of the respondents would not undergo any invasive procedures, and only 56% of the respondents would opt for noninvasive therapies to extend their healthy lifespans to 150 years of age if these were available.

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Creating portfolio-specific mortality tables: a case study

Cited by 15 publications

References 11 publications

A Bayesian Joint Model for Population and Portfolio-Specific Mortality

A Bayesian Joint Model for Population and Portfolio-Specific Mortality

Mis-estimation risk: measurement and impact

Longevity expectations in the pension fund, insurance, and employee benefits industries

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