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Longevity Risk

Edouard Debonneuil - AXA Global Life

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Longevity risk - AXA

1 Past, current and future longevity

2 Modelling and handling the longevity risk

3 Longevity risk at AXA

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1 Past, current

and future longevity

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Life expectancy increases by 6 hours per day

Oeppen & Vaupel. Science 2002; Christensen et al., Lancet 2009

Life Expectancy

record across countries

Life expectancy of 80 ≈expected lifespan of 100

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Why are we living longer?

Some potential explanations

A few key discoveries Louis Pasteur ideas: microbes & boiling water

Freezers, mass consumption, antibiotics

> Improvements for young/mid-life ages since 1800

A virtuous cycle Knowledge that biomedical research & prevention saves lives more

Many old persons focus on age-related diseases

> Improvements for higher ages since 1950

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A global longevity pandemic!L

ife

Ex

pec

tan

cy

Wealth

Evolutions from 1800 to 2007

High longevity

in developing

countries too

Some exceptions

http://gapminder.org

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Will longevity evolve similarly in the future?

Factors for acceleration

Biomedical improvements

- Smoking recession

- Focus on old age

- Internet and globalization

- Biology of aging

Increased awareness

- Government incentives

- Associations and conferences on health and longevity

- Genetic tests

Factors for a slow down Obesity, electromagnetic waves, pollution, natural catastrophes

Resistance to antibiotics and come back of infectious diseases

> Faster? Slower? Since it could be significantly

faster, there is a real longevity risk

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C.elegans | Humans

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Focus: an anti-aging breakthrough?

Normal worms One gene changed

The insulin-like pathway In worms, flies, yeast

Numerous life extensions in laboratories

2008: worm lifespan times nearly 10!

In mammals 2008: nearly doubled mouse lifespan (genetics+ diet restriction)

2009: a human drug (rapamycin) increases lifespan of aged mice

In Humans Natural mutations found in long-lived human cohorts, that extend

animal lives (IGF-1, FOXO or AKT): Hawaiians of Japanese descent,

Ashkenazi Jews, German centenarians, etc.

Testing on age-related diseases ongoing

Whether longevity drugs appear or remain probable,

in both cases this creates a high longevity risk

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Imagine…

The first man to

reach age 1000 is

perhaps already alive

It could be that we change

one single gene and

double our lifespan

Science is quickly developing the

technologies needed to radically

extend the quality human lifespan.

Cynthia KenyonAubrey de Grey

There is hope that, at some time

in the future, elderly people will

be kept healthy by suppressing

the ageing process itself. http://www.LifeStarInstitute.org

Healthier elderlies as a solution.(Science 1999, PMID: 10049123; BMJ 2008, PMID: 18614506)

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2 Modelling and handling the

longevity risk

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What is the longevity risk?

Living long is desirable – so why “longevity risk”?

Individual risk: at retirement, will you outlive your money?

“I’ve got all the money I need for my old age…

…provided I die before 4pm today”

Groucho Marx

- Also depends on market performance & costs of health

Solution: life insurers and private and public pension funds

take your longevity risk

Cheap: they act as intermediates:

- You pay before retirement, they pay you regular income after

- Based on mutualisation of risks

- And on a priori prudent estimates of average longevity

What if people live longer than expected?

> A global risk for the whole society.

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Demographic picture is worrying…

…For unfunded pensions Unfunded: workers pay for retired pensioners

Increased longevity + low birth rates increased longevity risk

For life insurance annuities this is less an issue Funded: money before retirement serves after retirement

Population aging might increase demand for annuities

1950 2000 2050

Percentage of 65+ :

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High amounts at stake

Life insurance annuities

US annuity sales in 2007

- Life & term : 73 bn$

- Indexed : 25 bn$

- Variable : 183 bn$

(Morningstar, Inc. and LIMRA International)

Crucial impact of models

One more year ≈ 3-4% reserves

USA, 2010, Medicare and Social Security: by 2050,

+ 3.1 to 7.9 years of life?

+ $3.2 trillion to $8.3 trillion?

(MacArthur Foundation Research Network on an Aging Society)

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Longevity modeling approaches

Some sound models prove wrong

Lower bounds for mortality rates- US 1928, L. Dublin: maximal life expectancy = 64.75 !

Extrapolating disease trends- They turn…

Some models proved OK: extrapolations of past mortality of general population to the future

General population- Fit past mortality (http://mortality.org)- Extrapolate (http://www.lifemetrics.com)

- Criteria to choose/reject models

Transposition to insurance- Insured population (mortality level and trend)- Extrapolation to high ages (e.g. Kannistö)

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Which patterns should a model capture?

Distribution of age at death

(Swiss females; Jean-Marie Robine, Le futur de la longévité en Suisse) Cohort effects

1850 2000

Insured characteristics Gender, profession, smoking status,

location. 1 cigarette = -11 min of life

May not be modeled

- Genetics: 25% of variations of lifespan

Age

Time (“period”)

Improvement factors

Age and time effects

Log mortality rates

E&W males

born 1920-

1940

General population

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Capturing patterns of past data

Various models

Interpretation: The global age-shape of mortality (β1

x)

Is positioned at lower and lower levels (κ 2t)

- With higher improvements for high ages (β2x)

Mortality is perturbated by cohort effects (γt-x)

Lee Carter (LC)

Cairns, Blake,

Down (CBD)

Lee Carter

+ Cohort (LCC)

http://arxiv.org/abs/1003.1802Main requirement: fit essential patterns

rather than patterns that won’t continue

in the future tradeoff between

complexity and robustness

Analyze fitting error

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Extrapolating the future

Fitted past Kappat

Projections of Kappat

Catastrophic scenario

Stochastic or deterministic

Quality of past predictions

qx

t

Coherence of new

best estimate

predictions

Lee Carter (LC)

Cairns, Blake,

Down (CBD)

Lee Carter

+ Cohort (LCC)

XX

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Handling the longevity risk

Business mix and “natural hedge” In case of high longevity, unused death benefit reserves may help

Caution and prudence

Solvency II framework Best estimate + Capital to put aside in case of high longevity

Keep an attentive eye Country by country, several times a year

Transfer excess longevity risk Longevity Swaps

Standardized financial products

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Principle

AXA has success stories in the UK Ongoing ones in other countries

Long process.

Reinsurance may become limited in absorbing longevity risks

Customized longevity Swap

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AXA, Deutsche Bank, J.P. Morgan, Legal & General, Morgan Stanley,

Pension Corporation, Prudential PLC, RBS, Swiss Re and UBS

Goal: create a liquid longevity market Financial markets = larger capital resources

Faster process

Standardized instruments Q-forwards (mortality) and S-forwards (survival)

Realized mortality/survival based on LLMA index

- UK as a first step

Life & Longevity Market Association (LLMA)

Fixed mortality/survival

Realized mortality/survival

Life Insurer Hedge Provider

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3 Longevity risk at AXA

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Longevity risk has to be handled

A risk to quantify AXA uses various models

No perfect quantification exists

Longevity risk is examined in details

AXA develops an internal Solvency II model

Attentiveness, business mix and risk transfer is key

Transferring solutions are possible AXA has done longevity risk transfers through longevity swaps

AXA participates in the LLMA to facilitate risk transfer

Accompanying progress is possible AXA Research Fund created a chair on Longevity

AXA Education and Research paper on Longevity

AXA Longevity Expert Network

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AXA - education and research

AXA Research Fund