Longevity Risk and Capital Markets:  The 2021-2022 Update

David Blake, Malene Kallestrup-Lamb and Jesper Rangvid

Abstract

This Special Issue of the Journal of Demographic Economics contains 10 contributions to the academic literature all dealing with longevity risk and capital markets. Draft versions of the papers were presented at Longevity 16: The Sixteenth International Longevity Risk and Capital Markets Solutions Conference that was held in Helsingør near Copenhagen on 13-14 August 2021. It was hosted by PerCent at Copenhagen Business School and the Pensions Institute at City, University of London.  Longevity risk and related capital market solutions have grown increasingly important in recent years, both in academic research and in the markets we refer to as the Life Market, i.e., the capital market that trades longevity-linked assets and liabilities.1 Mortality improvements around the world are putting more and more pressure on governments, pension funds, life insurance companies, as well as individuals, to deal with the longevity risk they face. At the same time, capital markets can, in principle, provide vehicles to hedge longevity risk effectively and transfer the risk from those unwilling or unable to manage it to those willing to invest in this risk in exchange for appropriate risk-adjusted returns or to those who have a counterpoising risk that longevity risk can hedge, e.g., life offices and reinsurers with mortality risk on their books. Many new investment products have been created both by the insurance/reinsurance industry and by the capital markets. Mortality catastrophe bonds are an early example of a successful insurance-linked security. Some new innovative capital market solutions for transferring longevity risk include longevity (or survivor) bonds, longevity (or survivor) swaps, mortality (or q-) forward contracts and reinsurance sidecars (also called strategic reinsurance vehicles). The aim of the International Longevity Risk and Capital Markets Solutions Conferences is to bring together academics and practitioners from all over  the world to discuss and analyze these exciting new developments.

Nudges and Networks:  How to use Behavioural Economics to Improve the Life Cycles Savings-Consumption Balance.

David Blake

Abstract:

Many people find it difficult to start and maintain a retirement savings plan.  We show how nudges can be used both the encourage people to save enough to provide an acceptable standard of living in retirement and to draw down their accumulated pension fund to maximize retirement spending, without the risk of either running out of money or leaving unintended bequests. Networks can help too, particularly employer-based networks. However, the nudges and networks are more likely to be effective if they have legislative backing and support.

Keywords:

nudges; networks; behavioural economics; life cycle savings-consumption; Save More Tomorrow (SMART) plans; Spend Optimally Throughout Retirement (SPEEDOMETER) plans.

JEL Classification:

D91

Good Practice Principles in Modelling Defined Contribution Pension Plans.

Kevin Dowd & David Blake

Abstract

We establish 16 Good practice principles for modelling defined contribution pension plans.  These principles cover the following issues: model specification and calibration; modelling quantifiable uncertainty; modelling member choices; modelling member characteristics, such as occupation and gender; modelling plan charges; modelling longevity risk; modelling the post retirement period; integrating the pre- and post-retirement periods; modelling additional sources of income, such as the state pension and equity release; modelling extraneous factors, such as unemployment risk, activity rates, taxes and welfare entitlements; scenario analysis and stress testing; periodic updating of the model and changing assumptions; and overall fitness for purpose.

Keywords:

defined contribution pension plans; PensionMetrics methodology; OECD Roadmap for the Good Design of Defined Contribution Pension Plans; EOPA Good practices on Information Provision for DC Schemes: Enabling Occupational DC Scheme Members to Plan for Retirement

JEL Classification:

C15; C18; C63; C68; D14; D91.

Projecting Mortality Rates to Extreme Old Age with the CBDX Model

Kevin Dowd and David Blake

Abstract

We introduce a simple extension to the CBDX model to project cohort mortality rates to extreme old age. The proposed approach fits a polynomial to a sample of age effects, uses the fitted polynomial to project the age effects to ages beyond the sample age range, then splices the sample and projected age effects, and uses the spliced age effects to obtain mortality rates for the higher ages. The proposed approach can be used to value financial instruments such as life annuities that depend on projections of extreme old age mortality rates.

Key Words:

mortality rates, Cairns-Blake-Dowd mortality model, CBDX mortality model, projection, extreme old age, life annuities

JEL Classification:

G22, G23, J11

Quantifying loss aversion: Evidence from a UK population survey

David Blake, Edmund Cannon & Douglas Wright.

Abstract

We quantify differences in attitudes to loss from individuals with different demographic, personal and socio-economic characteristics. Our data are based on responses from an online survey of a representative sample of over 4000 UK residents and allow us to produce the most comprehensive analysis of the heterogeneity of loss aversion measures to date. Using the canonical model proposed by Tversky and Kahneman (1992), we show that responses for the population as a whole differ substantially from those typically provided by students (who form the basis of many existing studies of loss aversion). The average aversion to a loss of £500 relative to a gain of the same amount is 2.41, but loss aversion correlates significantly with characteristics such as gender, age, education, financial knowledge, social class, employment status, management responsibility, income, savings and home ownership. Other related factors include marital status, number of children, ease of savings, rainy day fund, personality type, emotional state, newspaper and political party. However, once we condition on all the profiling characteristics of the respondents, some factors, in particular gender, cease to be significant, suggesting that gender differences in risk and loss attitudes might be due to other factors, such as income differences.

Keywords

Loss aversion · Gender effects · Expected utility · Risk attitudes · Survey data

JEL Classification

G40 · D40 · C83 · C90

A General Framework for Analysing the Mortality Experience of a Large Portfolio of Lives: With an Application to the UK Universities Superannuation Scheme

Andrew Cairns, David Blake, Kevin Dowd, Guy Coughlan, Owen Jones, and Jeffrey Rowney, Universities

Abstract

We report the results of an in-depth analysis of the mortality of pensioners in the Universities Superannuation Scheme (USS), the largest funded pension scheme in the UK and one with a highly educated and very homogeneous membership. The USS experience was compared with English mortality subdivided into deprivation deciles using the Index of Multiple Deprivation (IMD). USS was found to have significantly lower mortality than even IMD-10 (the least deprived of the English deciles), but with similar mortality improvement rates to that decile over the period 2005-2016. Higher pensions were found to predict lower mortality, but only weakly so, and only for persons who retired on the first day ofa month (mostly from active service). We found that other potential covariates derived from an individual’s postcode (geographical region and the IMD associated with their local area) typically had noexplanatory power, although there was some evidence of a north-south divide. This lack of dependence is an important conclusion of the study and contrasts with other that consider the mortality of more heterogeneous scheme memberships.   Keywords: Longevity Risk, Pensioners’ Mortality, Index of Multiple Deprivation, Age Standardised Mortality Rate, Occupation.

Smart  defaults:  Determining the number of default funds in a pension scheme

David Blake, Mel Duffield, Ian Tonks, Alistair Haig, Dean Blower & Laura MacPhee

Abstract

We propose a new methodology for the smart design of the default investment fund(s) in occupational defined contribution pension schemes based on the observable characteristics of scheme members. Using a unique dataset of member risk attitudes and characteristics from a survey of a large UK pension scheme, we apply factor analysis to identify single factors for risk aversion, risk capacity and ethical investment preferences, and then apply cluster analysis to these factors to identify two distinct groups of members across age cohorts. We find membership of these clusters depends on a number of personal characteristics, with the principal differentiating feature being that one group had previously engaged with the pension scheme, while the other had not. These identified characteristics can be utilised in the design of smart default funds, including appropriate engagement strategies.

Smart Defaults : Determining the number of default funds in a pension scheme.

David Blake, Mel Duffield, Ian Tonks, Alistair Haig, Dean Blower and Laura MacPhee.

Abstract

We propose a new methodology for the smart design of the default investment fund(s) in occupational defined contribution pension schemes based on the observable characteristics of scheme members. Using a unique dataset of member risk attitudes and characteristics from a survey of a large UK pension scheme, we apply factor analysis to identify single factors for risk aversion, risk capacity and ethical investment preferences, and then apply cluster analysis to these factors to identify two distinct groups of members across age cohorts. We find membership of these clusters depends on a number of personal characteristics, with the principal differentiating feature being that one group had previously engaged with the pension scheme, while the other had not. These identified characteristics can be utilised in the design of smart default funds, including appropriate engagement strategies.

Key words:

defined contribution pension schemes, investment choices, default investment funds, cluster analysis, risk attitude, risk capacity. JEL: G11, G41

Financial System Requirements for Successful Pension Reform

David Blake

Abstract

This paper examines the financial system prerequisites needed for the successful delivery of funded private pensions. In particular, it examines the financial instruments and investment strategies required during both the accumulation and decumulation stages. It does so within the context of a specific developed economy with a mature pension system, namely the United Kingdom. The lessons learned can help to inform the debate in developing countries that are in the process of undertaking pension reform.

Financing Development:Private Capital Mobilization and Institutional Investors

Georg Inderst

Abstract

This report discusses key issues around the mobilization of private capital for development.Investment requirements are huge, especially for infrastructure, climate and other SDGrelated investments. External finance for developing countries stagnated in the years beforethe pandemic, followed by a major setback in 2020/2021. The focus is in particular oninstitutional investors, whose exposure to less-developed countries is still very low, even more so in unlisted assets and projects. There is a potential for progress as asset owners seek new diversification opportunities in growth markets. The main burden is on governments to create favourable business conditions for investable long-term assets.Policy makers, development finance institutions and investors should utilize the full spectrum of investment vehicles – commercial, impact and blended finance.   JEL classification: F21, F3, G15, G18, G2, H54, H57, J32, L9, M14, O16, O18, 019, Q01, Q5 Keywords: development finance, private capital, institutional investors, emerging markets,blended finance, infrastructure investment, asset owners, impact investment, SDG investing, multilateral development banks, development finance institutions.