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Home > Financial Risk Management Best Practice > Financial Impact of Longevity Risk

Financial Risk Management Best Practice

Financial Impact of Longevity Risk

by S. Erik Oppers

This Chapter Covers

  • The concept of longevity risk.

  • Problems of forecasting longevity.

  • Quantification of the longevity risk faced by pension providers.

  • Ways to mitigate longevity risk through market-based transfer.

  • The role of government in facilitating longevity risk mitigation in the private sector.1

Introduction

Longevity risk is the risk that actual life spans of individuals or of whole populations will exceed expectations. People have been living longer lives for at least a century now, and this has obvious benefits. But governments, private companies, and individuals all potentially face financial risks if people on average live longer than expected. In particular, defined-benefit pension plans, insurance companies that offer life annuities, and governments that sponsor old-age social security systems would have to pay benefits for longer than anticipated, increasing the net present value of their liabilities. Individuals without a defined-benefit pension also face longevity risk, as they may run out of private pension savings if they live longer than expected.

The Difficulty of Forecasting Longevity

The main source of longevity risk is the discrepancy between expected and actual life spans, which has been large and one-sided: forecasters, regardless of the techniques they use, have consistently underestimated how long people will live. These forecast errors have been systematic over time and across populations. A study by the UK Office for National Statistics has evaluated the forecast errors made in the United Kingdom over the past decades (Figure 1). It showed that future estimates of longevity were consistently too low in each successive forecast, and errors were generally large (Shaw, 2007). In fact, underestimation is widespread across countries: 20-year forecasts of longevity made in recent decades in Australia, Canada, Japan, New Zealand, and the United States have been too low by an average of three years (Bongaarts and Bulatao, 2000). The systematic errors appear to arise from the assumption that currently observed rates of longevity improvement will slow down in the future (Box 1). In reality, they have not slowed down, partly because medical advances, such as better treatments for cancer and HIV, have continued to raise life expectancy.

The longevity risk resulting from these forecast errors is large and affects all of society. The International Monetary Fund (2012) calculated that if everyone in 2050 lived just three years longer than is now expected—three years being the average underestimation of longevity in the past—society would need extra resources equal to 1% to 2% of GDP per year. If this “longevity shock” occurred today and society wanted to save to pay for these extra resources for the next 40 years (that is, fully fund these additional “pension liabilities”), advanced countries would have to set aside around 50% of their 2010 GDP and emerging economies would need around 25% of 2010 GDP—a sum totaling tens of trillions of dollars. As such, longevity risk potentially adds one-half to the vast costs of aging up to the year 2050—and aging costs themselves are not fully recognized in most long-term fiscal plans.

Forecasting Longevity

Longevity forecasts can be made using various methods. Forecasting models can be broadly categorized into: methods that attempt to understand and use the underlying drivers of mortality (process-based methods and econometric models); and extrapolative methods, which use only historical trends to forecast future developments.

So-called process-based methods and econometric models seek an understanding of the underlying factors that drive death rates. These methods use biomedical assumptions to forecast death rates from various causes, leading to longevity rates for “cohorts” (people in a particular demographic section of the population born in a particular year or period). Econometric methods principally model longevity as a function of general economic, environmental, and epidemiological factors. A difficulty with both approaches is that they require a model for the relationship between underlying factors and longevity. Also, if they are used to make forecasts of longevity, forecasts need to be available for any underlying factors used in the model.

Extrapolative approaches do not attempt to identify the drivers of death rates but use only information contained in historical data to forecast future mortality rates. Such models could assume that historical trends continue going forward, either exactly or in some “smoothed” form, or could try to derive a more sophisticated model from historical trends (possibly disaggregated by cohort) that could then be used for a forecast. Methods can be deterministic—meaning that they directly calculate future changes from past trends—or stochastic, meaning that they apply random changes from a probability distribution derived from past developments to generate future changes. A drawback of the extrapolative approach is that it looks only at the past and does not use available information (or assumptions) about possible future developments that affect longevity, such as medical breakthroughs or changes in behavior.

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Further reading

Books:

  • Bongaarts, John, and Rodolfo A. Bulatao (eds). Beyond Six Billion: Forecasting the World’s Population. Washington, DC: National Academy Press, 2000.
  • International Monetary Fund. “The financial impact of longevity risk.” In Global Financial Stability Report: The Quest for Lasting Stability. Washington, DC: IMF, 2012; ch. 4. Online at: www.imf.org/external/pubs/ft/gfsr/2012/01/pdf/text.pdf

Articles:

  • Shaw, Chris. “Fifty years of United Kingdom national population projections: How accurate have they been?” Population Trends 128 (Summer 2007): 8–23. Online at: tinyurl.com/kph7me7 [PDF].
  • Sithole, T. Z., S. Haberman, and R. J. Verrall. “Second international comparative study of mortality tables for pension fund retirees.” British Actuarial Journal 17:3 (September 2012): 650–671. Online at: dx.doi.org/10.1017/S1357321712000207

Report:

  • Kisser, Michael, John Kiff, Erik S. Oppers, and Mauricio Soto. “The impact of longevity improvements on U.S. corporate defined benefit pension plans.” Working paper no. 12/170. IMF, June 2012. Online at: www.imf.org/external/pubs/ft/wp/2012/wp12170.pdf

Websites:

  • The Human Mortality Database (detailed population and mortality data for 37 countries): www.mortality.org

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