Modelling longevity bonds: Analysing the Swiss Re Kortis bond

Modeling mortality dependence for multiple populations has significant implications for mortality/longevity risk management. A natural way to assess multivariate dependence is to use copula models. The application of copula models in the multipopulation mortality analysis, however, is still in its infancy. In this article, we present a dynamic multipopulation mortality model based on a two‐factor copula and capture the time‐varying dependence using the generalized autoregressive score (GAS) framework. Our model is simple and flexible in terms of model specification and is widely applicable to high dimension data. Using the Swiss Re Kortis longevity trend bond as an example, we use our model to estimate the probability distribution of principal reduction and some risk measures such as probability of first loss, conditional expected loss, and expected loss. Due to the similarity in the structure and design of CAT bonds and mortality/longevity bonds, we borrow CAT bond pricing techniques for mortality/longevity bond pricing. We find that our pricing model generates par spreads that are close to the actual spreads of previously issued mortality/longevity bonds.

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“…Details of the LDIV construction are given inStandard & Poor's (2010). SeeHunt and Blake (2015) as well.…”

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confidence: 62%

Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model With the GAS Structure

Chen

MacMinn

Sun

2017

J of Risk & Insurance

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“…Projecting models with multiple age/period and cohort terms consistently is a difficult problem as the historical time series are often highly correlated and display curvature, outliers or subtle trend changes which need to be accommodated (as have been described in Li and Chan (2005); Li et al (2011) and Coelho and Nunes (2011)). We therefore intend to address this issue in Hunt and Blake (2015a) and Hunt and Blake (2015d).…”

Section: Modelmentioning

confidence: 99%

A General Procedure for Constructing Mortality Models

Hunt

Blake

2015

SSRN Journal

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Amended 20 January 2015 Permanent repository link AbstractRecently, a large number of new mortality models have been proposed to analyse historical mortality rates and project them into the future. Many of these suffer from being over-parametrised or have terms added in an ad hoc manner which cannot be justified in terms of demographic significance. In addition, poor specification of a model can lead to period effects in the data being wrongly attributed to cohort effects which results in the model making implausible projections. We present a general procedure for constructing mortality models using a combination of a toolkit of functions and expert judgement. By following the general procedure, it is possible to identify sequentially every significant demographic feature in the data and give it a parametric structural form. We demonstrate using UK mortality data that the general procedure produces a relatively parsimonious model that nevertheless has a good fit to the data. * We are grateful to participants at a seminar at Cass

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“…Substantial work has been produced to price longevity risk and to propose financial tools such as bonds and swaps to transfer the risk to specific stakeholders or even capital markets (Barrieu et al 2012, Bauer et al 2010, Barbarin 2008, Blake et al 2006, Hunt and Blake 2015, Kogure and Kurachi 2010, Lane 2011, Ngai and Sherris 2011, Olivieri and Pitacco 2008, Wan and Bertschi 2015.…”

Section: Financial Hedges To Help Mitigate Longevity Riskmentioning

confidence: 99%

Do actuaries believe in longevity deceleration?

Debonneuil

Loisel

Planchet

2018

Insurance: Mathematics and Economics

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As more and more people believe that significant life extensions may come soon, should commonly used future mortality assumptions be considered prudent? We find here that commonly used actuarial tables for annuitants -as well as the Lee-Carter model -do not extrapolate life expectancy at the same rate for future years as for past years; instead they produce some longevity deceleration. This is typically because their mortality improvements decrease after a certain age, and those age-specific improvements are constant over time. As potential alternatives i) we study the Bongaarts model that produces straight increases in life expectancy; ii) we adapt it to produce best-practice longevity trends iii) we compare with various longevity scenarios even including a model for "life extension velocity". iv) after gathering advances in biogerontology we discuss elements to help retirement systems cope with a potential strong increase in life expectancy.

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Modelling longevity bonds: Analysing the Swiss Re Kortis bond

Cited by 41 publications

References 52 publications

Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model With the GAS Structure

Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model With the GAS Structure

A General Procedure for Constructing Mortality Models

Do actuaries believe in longevity deceleration?

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