Assessing Basis Risk for Longevity Transactions

Li, Jackie Ka Ki; Tickle, Leonie; Tan, Chong It

doi:10.2139/ssrn.3077651

Cited by 2 publications

(6 citation statements)

References 21 publications

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“…The study concluded that a version of the CBD model allowing for a cohort effect was found to have the most robust and stable parameter estimates over time using mortality data from both England & Wales and the US. This model (usually referred to as “M7”) is now the keystone of one of the two approaches recommended by the Life and Longevity Markets Association (LLMA) 62 (Longevity Basis Risk Working Group, 2014, Villegas et al ., 2017, and Li et al ., 2017).…”

Section: Mortality Modelsmentioning

confidence: 99%

“…In May 2016, a follow-up study – carried out by Macquarie University, Mercer Australia, and the University of Waterloo – was announced. The purpose was to design a “readily applicable methodology” for use with longevity risk indices: “Such indices are often used in pension benefits and annuitant liabilities, as well as in providing actuaries with key data, …but the problem of the existence of basis risk remains unsolved.” This follow-up study was published in December 2017 (Li et al ., 2017). The report distinguishes between three types of basis risk (population, sampling and structural basis risk) and takes the proposed models in the 2014 report through to full analysis of hedge effectiveness.…”

Section: Developments In the Longevity Risk Transfer Market Since 2006mentioning

confidence: 99%

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Still living with mortality: the longevity risk transfer market after one decade

Blake¹,

Cairns²,

Dowd³

et al. 2019

Br. Actuar. J.

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This paper updates Living with Mortality published in 2006. It describes how the longevity risk transfer market has developed over the intervening period, and, in particular, how insurance-based solutions – buy-outs, buy-ins and longevity insurance – have triumphed over capital markets solutions that were expected to dominate at the time. Some capital markets solutions – longevity-spread bonds, longevity swaps, q-forwards and tail-risk protection – have come to market, but the volume of business has been disappointingly low. The reason for this is that when market participants compare the index-based solutions of the capital markets with the customised solutions of insurance companies in terms of basis risk, credit risk, regulatory capital, collateral and liquidity, the former perform on balance less favourably despite a lower potential cost. We discuss the importance of stochastic mortality models for forecasting future longevity and examine some applications of these models, e.g. determining the longevity risk premium and estimating regulatory capital relief. The longevity risk transfer market is now beginning to recognise that there is insufficient capacity in the insurance and reinsurance industries to deal fully with demand and new solutions for attracting capital markets investors are now being examined – such as longevity-linked securities and reinsurance sidecars.

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Section: Mortality Modelsmentioning

confidence: 99%

Section: Developments In the Longevity Risk Transfer Market Since 2006mentioning

confidence: 99%

Still living with mortality: the longevity risk transfer market after one decade

Blake¹,

Cairns²,

Dowd³

et al. 2019

Br. Actuar. J.

View full text Add to dashboard Cite

show abstract

“…Bajekal (2005), Lu et al (2014)) or in mortality modeling (e.g. Villegas and Haberman (2014) and Li et al (2017)). In addition to updating the data for years 2007-2015, Dataset 2 provides disaggregated data by single year of age as well as data for ages below 25.…”

Section: Datasetsmentioning

confidence: 99%

“…As mortality data are more commonly studied, we only give here a brief overview of the main stylized facts, with a particular focus on Dataset 2. For more details on the mortality data, we refer to Bajekal (2005), Lu et al (2014) or Villegas (2015) for Dataset 1 and Li et al (2017) for Dataset 2.…”

Section: Mortalitymentioning

confidence: 99%

“…The widening of these socioeconomic gaps has led a number of pension funds and insurance companies to rethink their models in order to tackle this heterogeneity issue and to understand the potential impact of socioeconomic inequalities. For instance, the Life & Longevity Markets Association (LLMA) and the Institute and Faculty of Actuaries (IFoA) have recently commissioned a series of reports on longevity basis risk (Haberman et al (2014), Li et al (2017)). Indeed, not taking into account socioeconomic differences can have substantial impacts for insurance companies or governments, by leading, for instance, to errors in funding of annuity and pension obligations (see e.g.…”

Section: Introductionmentioning

confidence: 99%

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How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach

Kaakaï

Hardy²,

Arnold

2019

Insurance: Mathematics and Economics

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A growing number of studies indicate a widening of socioeconomic inequalities in mortality over the past decades. It has therefore become crucially important to understand the impact of heterogeneity and its evolution on the future mortality of heterogeneous populations. In particular, recent developments in multi-population mortality have raised a number of questions, among which is the issue of evaluating cause-of-death reduction targets set by national and international institutions in the presence of heterogeneity. The aim of this paper is to show how the study of the population data and the population dynamics framework contribute to addressing these issues, by providing a new viewpoint on the evolution of aggregate mortality indicators in the presence of heterogeneity. Our findings rely on two unique datasets on the English population and cause-specific number of deaths by socioeconomic circumstances, over the period 1981-2015. The analysis of the data first highlights the complexity of recent demographic developments, characterized by significant composition changes in the population, with considerable variations according to the age class or cohort, along with a widening of socioeconomic inequalities. We then introduce a dynamic framework for studying the impact of composition changes on the mortality of the global population. In particular, we are interested in quantifying the impacts of cause-of-death mortality reduction in comparison with changes of composition in a heterogeneous population. We show how a cause of death reduction could be compensated for in the presence of heterogeneity, which could lead to misinterpretations when assessing public policies impacts and/or for the forecasting of future trends.

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Assessing Basis Risk for Longevity Transactions

Cited by 2 publications

References 21 publications

Still living with mortality: the longevity risk transfer market after one decade

Still living with mortality: the longevity risk transfer market after one decade

How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach

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