Computational framework for longevity risk management

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract: Longevity risk threatens the financial stability of private and government sponsored defined benefit pension systems as well as social security schemes, in an environment already characterized by persistent low interest rates and heightened financial uncertainty. Permanent repository linkThe mortality experience of countries in the industrialized world 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. It is observed that mortality improvements are similar for individuals of contiguous ages (Wills and Sherris 2008). Moreover, considering the dataset by single ages, 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.In this research, we focus on the projections of mortality rates, contravening the most commonly encountered dependence property which is the "lack of dependence" . By taking into account the presence of dependence across age and time which leads to systematic over-estimation or under-estimation of uncertainty in the estimates (Liu and Braun 2010), the paper analyzes a tailor-made bootstrap methodology for capturing the spatial dependence in deriving prediction intervals for mortality projection rates. We propose a method which leads to a prudent measure of longevity risk, avoiding the structural incompleteness of the ordinary simulation bootstrap methodology which involves the assumption of independence.Response to Reviewers: Dear Referees, we thank you for your reports, which were useful for us, in order to increase the value of our work. Please find below, the description of how we have changed the paper according to your suggestions.Reviewer #1: General comments Powered by Editorial Manager® and Preprint Manager® from Aries Systems CorporationThe manuscript "Computational framework for longevity risk management" introduces a Panel Sieve Bootstrapping technique in the Lee-Carter setting to capture the spatial dependence across age and time in deriving prediction intervals for mortality projection rates. The manuscript is well written and the information provided is so interesting. The use of bootstrapping method to explore the dependency nature of residuals looks very promising for me. However, I found some difficulties to prove the accurate projection with reducing dependency nature of the residual using bootstrapping technique. The authors started nicely to describe the procedure; however, failed to prove the evidence of forecast accuracy using their techniques.We have added some comments on forecast accuracy at the end of the section 4. In particular, we have enriched the numerical application with the calculation and comments ...

The dependency premium based on a multifactor model for dependent mortality data

“…The structure of the dependence present in mortality data cannot be ignored, in order to obtain reliable projections as demonstrated by D'Amato et al (2012D'Amato et al ( , 2014aD'Amato et al ( . 2014bD'Amato et al ( , 2016.…”