Multiple mortality modeling in Poisson Lee-Carter framework

The academic literature in longevity field has recently focused on models for detecting multiple population trends (D'Amato et al., 2012b; Njenga and Sherris, 2011; Russolillo et al., 2011, etc.). In particular, increasing interest has been shown about "related" population dynamics or "parent" populations characterized by similar socioeconomic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long-run relationships between countries (for instance, see Lazar et al., 2009). In order to investigate cross-country longevity common trends, we adopt a multiple population approach. The algorithm we propose retains the parametric structure of the Lee-Carter model, extending the basic framework to include some cross-dependence in the error term. As far as time dependence is concerned, we allow for all idiosyncratic components (both in the common stochastic trend and in the error term) to follow a linear process, thus considering a highly flexible specification for the serial dependence structure of our data. We also relax the assumption of normality, which is typical of early studies on mortality (Lee and Carter, 1992) and on factor models (see e.g., the textbook by Anderson, 1984). The empirical results show that the multiple Lee-Carter approach works well in the presence of dependence

Sex-specific mortality forecasting for UK countries: a coherent approach

This paper introduces a gender specific model for the joint mortality projection of three countries (England and Wales combined, Scotland, and Northern Ireland) of the United Kingdom. The model, called 2-tier Augmented Common Factor model, extends the classical Lee and Carter [26] and Li and Lee [32] models, with a common time factor for the whole UK population, a sex specific period factor for males and females, and a specific time factor for each country within each gender. As death counts in each subpopulation are modelled directly, a Poisson framework is used. Our results show that the 2-tier ACF model improves the in-sample fitting compared to the use of independent LC models for each subpopulation or of independent Li and Lee models for each couple of genders within each country. Mortality projections also show that the 2-tier ACF model produces coherent forecasts for the two genders within each country and different countries within each gender, thus avoiding the divergence issues arising when independent projections are used. The 2-tier ACF is further extended to include a cohort term to take into account the faster improvements of the UK ‘golden generation’

Who still dies young in a rich city? Revisiting the case of Oxford

There are substantial inequalities in mortality and life expectancy in England, strongly linked to levels of deprivation. Mortality rates among those who are homeless are particularly high. Using the city of Oxford (UK) as a case study, we investigate ward-level premature standardised mortality ratios for several three-year and five-year periods between 2002 and 2016, and explore the extent to which the mortality of people who become homeless contributed to any rise or fall in geographical inequalities during this period. Age–sex standardised mortality ratios (SMRs) for people aged under 65 years old, with and without deaths among the homeless population, were calculated using Office for National Statistics Death Registration data for England and Wales 2002−2016. Individuals who were homeless or vulnerably housed were identified using records supplied by a local Oxford homeless charity. We found that in an increasingly wealthy, and healthy, city there were persistent ward-level inequalities in mortality, which the city-wide decrease in premature mortality over the period masked. Premature deaths among homeless people in Oxford became an increasingly important contributor to the overall geographical inequalities in health in this city. In the ward with the highest SMR, deaths among the homeless population accounted for 73% of all premature deaths of residents over the whole period; in 2014–2016 this proportion rose to 88%. Homelessness among men (the vast majority of the known homeless population) in this gentrifying English city rose to become the key explanation of geographical mortality patterns in deaths before age 65 across the entire city, particularly after 2011. Oxford reflects a broader pattern now found in many places across England of increasing homeless deaths, widening geographical inequalities in life expectancy, and sharp increases in all-age SMRs. The answer to the question, “Who dies young in a rich, and in fact an even richer, place?” is – increasingly – the homeless

The Princeton Protein Orthology Database (P-POD): A Comparative Genomics Analysis Tool for Biologists

Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools

Utility maximization under solvency constraints and unhedgeable risks

We consider the utility maximization problem for an investor who faces a solvency or risk constraint in addition to a budget constraint. The investor wishes to maximize her expected utility from terminal wealth subject to a bound on her expected solvency at maturity. We measure solvency using a solvency function applied to the terminal wealth. The motivation for our analysis is an optimal investment problem where the investor faces a random and non-hedgable liability at maturity. Keywords: Utility maximisation, Risk constraint, Value-at-Risk, Merton problem, Expected shortfall, Tail-Value-at-Ris

Financial fairness and conditional indexation

Collective pension contracts can generate advantages for their participants by implementing forms of risk sharing. To ensure the continuity of a collective scheme, it has to be monitored whether the contracts offered to participants are financially fair in terms of their market value. When risk sharing is implemented by means of optionalities such as conditional indexation, the analysis of financial fairness is not straightforward. In this paper, we use a stylised overlapping generations model to study financial fairness for a conditional indexation scheme. We find that financial fairness for all participants at all times is not feasible within a scheme of this type, unless the nature of indexation is such that the scheme is reduced to DC. However, financial fairness for incoming generations at the moment of entry can be realised. We show how to compute the fair contribution rate as a function of the current nominal asset/liability ratio for a given level of nominal entitlements. At low levels of the ratio, the fair contribution for incoming generations is also relatively low; nevertheless, the joining of a new generation still has a positive effect on the asset/liability ratio