scholarly journals MODELLING MORTALITY FOR PENSION SCHEMES

2017 ◽  
Vol 47 (2) ◽  
pp. 601-629 ◽  
Author(s):  
Andrew Hunt ◽  
David Blake
Keyword(s):  
Population Data ◽  
Reference Population ◽  
Mortality Rates ◽  
Pension Scheme ◽  
Basis Risk ◽  
Stochastic Mortality ◽  
Pension Schemes ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Two Populations

AbstractFor many pension schemes, a shortage of data limits their ability to use sophisticated stochastic mortality models to assess and manage their exposure to longevity risk. In this study, we develop a mortality model designed for such pension schemes, which compares the evolution of mortality rates in a sub-population with that observed in a larger reference population. We apply this approach to data from the CMI Self-Administered Pension Scheme study, using U.K. population data as a reference. We then use the approach to investigate the potential differences in the evolution of mortality rates between these two populations and find that, in many practical situations, basis risk is much less of a problem than is commonly believed.

scholarly journals A COMPARATIVE STUDY OF TWO-POPULATION MODELS FOR THE ASSESSMENT OF BASIS RISK IN LONGEVITY HEDGES

2017 ◽  
Vol 47 (3) ◽  
pp. 631-679 ◽  
Author(s):  
Andrés M. Villegas ◽  
Steven Haberman ◽  
Vladimir K. Kaishev ◽  
Pietro Millossovich
Keyword(s):  
Cost Effective ◽  
Pension Scheme ◽  
Basis Risk ◽  
Mortality Experience ◽  
Market Participants ◽  
Stochastic Mortality ◽  
Success Stories ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Data Requirements

AbstractLongevity swaps have been one of the major success stories of pension scheme de-risking in recent years. However, with some few exceptions, all of the transactions to date have been bespoke longevity swaps based upon the mortality experience of a portfolio of named lives. In order for this market to start to meet its true potential, solutions will ultimately be needed that provide protection for all types of members, are cost effective for large and smaller schemes, are tradable, and enable access to the wider capital markets. Index-based solutions have the potential to meet this need; however, concerns remain with these solutions. In particular, the basis risk emerging from the potential mismatch between the underlying forces of mortality for the index reference portfolio and the pension fund/annuity book being hedged is the principal issue that has, to date, prevented many schemes progressing their consideration of index-based solutions. Two-population stochastic mortality models offer an alternative to overcome this obstacle as they allow market participants to compare and project the mortality experience for the reference and target populations and thus assess the amount of demographic basis risk involved in an index-based longevity hedge. In this paper, we systematically assess the suitability of several multi-population stochastic mortality models for assessing basis risks and provide guidelines on how to use these models in practical situations paying particular attention to the data requirements for the appropriate calibration and forecasting of such models.

scholarly journals Identifying Structural Breaks in Stochastic Mortality Models

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Colin O’Hare ◽  
Youwei Li
Keyword(s):  
Life Expectancy ◽  
Structural Breaks ◽  
Structural Break ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Mortality Data ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Mortality Forecasts

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

scholarly journals Application of Machine Learning to Mortality Modeling and Forecasting

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 26 ◽  
Author(s):  
Susanna Levantesi ◽  
Virginia Pizzorusso
Keyword(s):  
Machine Learning ◽  
Stochastic Models ◽  
Goodness Of Fit ◽  
Mortality Rates ◽  
Machine Learning Algorithms ◽  
Stochastic Mortality ◽  
Life Insurers ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Out Of Sample

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.

Evaluating the Goodness of Fit of Stochastic Mortality Models

2009 ◽  
Author(s):  
Kevin Dowd ◽  
Andrew J. G. Cairns ◽  
David P. Blake ◽  
Guy Coughlan ◽  
David Epstein ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models

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

2008 ◽  
Author(s):  
Kevin Dowd ◽  
Andrew J. G. Cairns ◽  
David P. Blake ◽  
Guy Coughlan ◽  
David Epstein ◽  
...  
Keyword(s):  
Stochastic Mortality ◽  
Post Evaluation ◽  
Ex Post ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Density Forecasts

scholarly journals Mortality Density Forecasts: An Analysis of Six Stochastic Mortality Models

2008 ◽  
Author(s):  
Andrew J. G. Cairns ◽  
David P. Blake ◽  
Kevin Dowd ◽  
Guy Coughlan ◽  
David Epstein ◽  
...  
Keyword(s):  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Density Forecasts

Backtesting Stochastic Mortality Models

2010 ◽  
Vol 14 (3) ◽  
pp. 281-298 ◽  
Author(s):  
Kevin Dowd ◽  
Andrew J. G. Cairns ◽  
David Blake ◽  
Guy D. Coughlan ◽  
David Epstein ◽  
...  
Keyword(s):  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models

scholarly journals Mortality Projections for Small Populations: An Application to the Maltese Elderly

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 35
Author(s):  
Massimiliano Menzietti ◽  
Maria Morabito ◽  
Manuela Stranges
Keyword(s):  
Missing Data ◽  
Older Adult ◽  
Reference Population ◽  
Mortality Rates ◽  
Adult Mortality ◽  
Small Population ◽  
Small Populations ◽  
Small Country ◽  
Mortality Models ◽  
Mortality Projections

In small populations, mortality rates are characterized by a great volatility, the datasets are often available for a few years and suffer from missing data. Therefore, standard mortality models may produce high uncertain and biologically improbable projections. In this paper, we deal with the mortality projections of the Maltese population, a small country with less than 500,000 inhabitants, whose data on exposures and observed deaths suffers from all the typical problems of small populations. We concentrate our analysis on older adult mortality. Starting from some recent suggestions in the literature, we assume that the mortality of a small population can be modeled starting from the mortality of a bigger one (the reference population) adding a spread. The first part of the paper is dedicated to the choice of the reference population, then we test alternative mortality models. Finally, we verify the capacity of the proposed approach to reduce the volatility of the mortality projections. The results obtained show that the model is able to significantly reduce the uncertainty of projected mortality rates and to ensure their coherent and biologically reasonable evolution.

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