1,721,055 research outputs found
Variable Annuities and Embedded Options: models and tools for fair valuation and solvency appraisal
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Multiple Population Projections by Lee Carter Models
No full textThe academic literature in longevity field has recently focused on models for detecting multiple population trends ([9],[17],[20], etc.). In particular increasing interest has been shown about “related” population dynamics or “parent” populations characterized by similar socio-economic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long run relationships between countries (for instance see [13]). 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 [14] and on factor models (see e.g. the textbook by [1]). The empirical results show that the Multiple Lee Carter Approach works well in presence of dependence
Modelling Dependent Data For Longevity Projections
No full textThe risk profile of an insurance company involved in annuity business is heavily affected by the
uncertainty in future mortality trends. It is problematic to capture accurately future survival patterns,
in particular at retirement ages when the effects of the rectangularization phenomenon and random
fluctuations are combined. Another important aspect affecting the projections is related to the so-called
cohort-period effect. In particular, the mortality experience of countries in the industrialized world over
the course of the twentieth century would suggest a substantial age–time interaction, with the two
dominant trends affecting different age groups at different times. From a statistical point of view, this
indicates a dependence structure. Also the dependence between ages is an important component in the
modeling of mortality (Barrieu et al., 2011). It is observed that the mortality improvements are similar for
individuals of contiguous ages (Wills and Sherris, 2008). Moreover, considering the data subdivided by
set by single years of age, the correlations between the residuals for adjacent age groups tend to be high
(as noted in Denton et al., 2005). This suggests that there is value in exploring the dependence structure,
also across time, in other words the inter-period correlation. The aim of this paper is to improve the
methodology for forecasting mortality in order to enhance model performance and increase forecasting
power by capturing the dependence structure of neighboring observations in the population. To do
this, we adapt the methodology for measuring uncertainty in projections in the Lee–Carter context and
introduce a tailor-made bootstrap instead of an ordinary bootstrap. The approach is illustrated with an
empirical example
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