Guy D. Coughlan

A quantitative comparison of stochastic mortality models using data from England and wales and the United States

Abstract We compare quantitatively eight stochastic models explaining improvements in mortality rates in England and Wales and in the United States. On the basis of the Bayes Information Criterion (BIC), we find that, for higher ages, an extension of the Cairns-Blake-Dowd (CBD) model that incorporates a cohort effect fits the England and Wales males data best, while for U.S. males data, the Renshaw and Haberman (RH) extension to the Lee and Carter model that also allows for a cohort effect provides the best fit. However, we identify problems with the robustness of parameter estimates under the RH model, calling into question its suitability for forecasting. A different extension to the CBD model that allows not only for a cohort effect, but also for a quadratic age effect, while ranking below the other models in terms of the BIC, exhibits parameter stability across different time periods for both datasets. This model also shows, for both datasets, that there have been approximately linear improvements over time in mortality rates at all ages, but that the improvements have been greater at lower ages than at higher ages, and that there are significant cohort effects.

Bayesian Stochastic Mortality Modelling for Two Populations

This paper introduces a new framework for modelling the joint development over time of mortality rates in a pair of related populations with the primary aim of producing consistent mortality forecasts for the two populations. The primary aim is achieved by combining a number of recent and novel developments in stochastic mortality modelling, but these, additionally, provide us with a number of side benefits and insights for stochastic mortality modelling. By way of example, we propose an Age-Period-Cohort model which incorporates a mean-reverting stochastic spread that allows for different trends in mortality improvement rates in the short-run, but parallel improvements in the long run. Second, we fit the model using a Bayesian framework that allows us to combine estimation of the unobservable state variables and the parameters of the stochastic processes driving them into a single procedure. Key benefits of this include dampening down of the impact of Poisson variation in death counts, full allowance for paramater uncertainty, and the flexibility to deal with missing data. The framework is designed for large populations coupled with a small sub-population and is applied to the England & Wales national and Continuous Mortality Investigation assured lives males populations. We compare and contrast results based on the two-population approach with single-population results.

Backtesting Stochastic Mortality Models: An Ex-Post Evaluation of Multi-Period Ahead-Density Forecasts

Abstract This study sets out a backtesting framework applicable to the multiperiod-ahead forecasts from stochastic mortality models and uses it to evaluate the forecasting performance of six different stochastic mortality models applied to English & Welsh male mortality data. The models considered are the following: Lee-Carter’s 1992 one-factor model; a version of Renshaw-Haberman’s 2006 extension of the Lee-Carter model to allow for a cohort effect; the age-period-cohort model, which is a simplified version of Renshaw-Haberman; Cairns, Blake, and Dowd’s 2006 two-factor model; and two generalized versions of the last named with an added cohort effect. For the data set used herein, the results from applying this methodology suggest that the models perform adequately by most backtests and that prediction intervals that incorporate parameter uncertainty are wider than those that do not. We also find little difference between the performances of five of the models, but the remaining model shows considerable forecast instability.

A Gravity Model of Mortality Rates for Two Related Populations

Abstract The mortality rate dynamics between two related but different-sized populations are modeled consistently using a new stochastic mortality model that we call the gravity model. The larger population is modeled independently, and the smaller population is modeled in terms of spreads (or deviations) relative to the evolution of the former, but the spreads in the period and cohort effects between the larger and smaller populations depend on gravity or spread reversion parameters for the two effects. The larger the two gravity parameters, the more strongly the smaller population’s mortality rates move in line with those of the larger population in the long run. This is important where it is believed that the mortality rates between related populations should not diverge over time on grounds of biological reasonableness. The model is illustrated using an extension of the Age-Period-Cohort model and mortality rate data for English and Welsh males representing a large population and the Continuous Mortality Investigation assured male lives representing a smaller related population.

Longevity hedging 101: A framework for longevity basis risk analysis and hedge effectiveness

Abstract Basis risk is an important consideration when hedging longevity risk with instruments based on longevity indices, since the longevity experience of the hedged exposure may differ from that of the index. As a result, any decision to execute an index-based hedge requires a framework for (1) developing an informed understanding of the basis risk, (2) appropriately calibrating the hedging instrument, and (3) evaluating hedge effectiveness. We describe such a framework and apply it to a U.K. case study, which compares the population of assured lives from the Continuous Mortality Investigation with the England and Wales national population. The framework is founded on an analysis of historical experience data, together with an appreciation of the contextual relationship between the two related populations in social, economic, and demographic terms. Despite the different demographic profiles, the case study provides evidence of stable long-term relationships between the mortality experiences of the two populations. This suggests the important result that high levels of hedge effectiveness should be achievable with appropriately calibrated, static, index-based longevity hedges. Indeed, this is borne out in detailed calculations of hedge effectiveness for a hypothetical pension portfolio where the basis risk is based on the case study. A robustness check involving populations from the United States yields similar results.

Hedging Pension Longevity Risk: Practical Capital Markets Solutions

Longevity risk transfer via the capital markets is now a reality. Pension plans and annuity providers can hedge longevity risk with capital markets instruments, reflecting the emergence of a new market that is poised to take off. The key players in this market are hedgers (pension plans and annuity providers), intermediaries (investment banks and broker-dealers) and end investors (ILS funds, hedge funds, endowments, etc.). We argue that the development of liquidity in this market depends on the acceptance of longevity indices and the development of standardized instruments to transfer this risk.Until now, hedgers of longevity risk have almost exclusively approached the subject from the perspective of indemnification (100 percent risk transfer). We propose an alternative approach based on a risk management paradigm that does not require 100 percent risk transfer and is consistent with the way in which other pension-related risks are managed. To this end we present a framework for longevity hedging cantered on standardized indexbased hedges. This framework uses a building-block approach in which standardized hedge building blocks are combined to provide a longevity hedge tailored to the specific demographics, benefit structure and mortality table of any pension plan. The effectiveness of this hedge is maximized by careful calibration of the mix of building blocks and then verified in hedge effectiveness tests.We also discuss customized longevity hedges that will be preferred by some hedgers, who are unconcerned by the lower liquidity and onerous requirements for data disclosure associated with these hedges, and are prepared to pay the additional premium above the cost of a standardized hedge.

Longevity Risk and Hedging Solutions

Longevity risk—the risk of unanticipated increases in life expectancy—has only recently been recognized as a significant global risk that has materially raised the costs of providing pensions and annuities. We first discuss historical trends in the evolution of life expectancy and then analyze the hedging solutions that have been developed for managing longevity risk. One set of solutions has come directly from the insurance industry: pension buyouts, buy-ins, and bulk annuity transfers. Another complementary set of solutions has come from the capital markets: longevity swaps and q-forwards. This has led to hybrid solutions such as synthetic buy-ins. We then review the evolution of the market for longevity risk transfer, which began in the UK in 2006 and is arguably the most important sector of the broader “life market.” An important theme in the development of the longevity market has been the innovation originating from the combined involvement of insurance, banking, and private equity participants.