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1
MODELLING AND MANAGEMENT OF MORTALITY
RISK
Stochastic models for modelling mortality risk
ANDREW CAIRNS
Heriot-Watt University, Edinburgh
and
Director of the Actuarial Research Centre
Institute and Faculty of Actuaries
2
Actuarial Research Centre (ARC)
The Actuarial Research Centre (ARC) is the Institute and Faculty of Actuaries’
network of actuarial researchers around the world.
The ARC seeks to deliver research programmes that bridge academic rigour with
practitioner needs by working collaboratively with academics, industry and other
actuarial bodies.
The ARC supports actuarial researchers around the world in the delivery of
cutting-edge research programmes that aim to address some of the significant
challenges in actuarial science.
www.actuaries.org.uk/ARC
3
Actuarial Research Centre (ARC)
Current research programmes (2016-2021)
• Modelling, Measurement and Management of Longevity and Morbidity Risk
• Use of Big Health and Actuarial Data for understanding Longevity and
Morbidity
• Minimising Longevity and Investment Risk While Optimising Future Pension
Plans
4
Stochastic models for modelling mortality risk: Plan
• Introduction, motivation, problems
• Modelling
– Criteria for a good model
– Comparison of 8 models
– Robustness
– Graphical diagnostics
• Applications
5
The Problem
2017: What we know as the facts:
• Life expectancy is increasing.
• Future development of life expectancy is uncertain.
“Longevity risk”
⇒ Systematic risk for pension plans and annuity providers
6
The Problem
Example: UK Defined-Benefit Pension Plans
• Before 2000:
– High equity returns masked impact of longevity improvements
• After 2000:
– Poor equity returns, low interest rates
– Decades of longevity improvements now a problem
7England and Wales males mortality (log scale)
1900 1940 1980
0.00
10.
002
0.00
4
Age = 25
Year
Mor
talit
y ra
te
1900 1940 1980
0.00
50.
010
0.02
0
Age = 45
Year
Mor
talit
y ra
te
1900 1940 1980
0.05
0.10
Age = 65
Year
Mor
talit
y ra
te
1900 1940 1980
0.2
0.4
0.8
Age = 85
Year
Mor
talit
y ra
te
8
Graphical diagnostics
• Mortality is falling
• Different improvement rates at different ages
• Different improvement rates over different periods
• Improvements are random
– Short term fluctuations
– Long term trends
• All stylised facts
9
STOCHASTIC MORTALITY
n lives, probability p of survival, N survivors
• Unsystematic mortality risk:
⇒N |p ∼ Binomial(n, p)
⇒ risk is diversifiable, N/n → p as n → ∞
• Systematic mortality risk:
⇒ p is uncertain
⇒ risk associated with p is not diversifiable
• Longevity Risk: the risk that in aggregate people live longer than
anticipated.
10
Why do we need stochastic mortality models?
Data ⇒ future mortality is uncertain
• Good risk management
• Setting risk reserves
• Regulatory capital requirements (e.g. Solvency II)
• Life insurance contracts with embedded options
• Pricing and hedging mortality-linked securities
11
Modelling
Aims:
• to develop the best models for forecasting future uncertain
mortality;
– general desirable criteria
– complexity of model ↔ complexity of problem;
– longevity versus brevity risk;
• measurement of risk;
• valuation of future risky cashflows.
12
Management
Aims:
• active management of mortality and longevity risk;
– internal (e.g. product design; natural hedging)
– over-the-counter deals (OTC)
– securitisation
• part of overall package of good risk management.
13
Modelling Stochastic Mortality
• Many models to choose from
• Limited data ⇒ model and parameter risk
• Important to take the time to analyse models thoroughly
• No single model is best for all datasets and applications
14
Model Selection Criteria• Positive mortality rates
• Consistent with historical data
• Biologically reasonable and plausible forecasts
• Robust parameter estimates and forecasts
• Straightforward to implement
• Parsimonious
• Generates sample paths
• Can include parameter uncertainty
• Cohort effect if appropriate
• Non-trivial correlation structure
• Not used as a black box
15
Consistent with historical data
• Model fit consistent with i.i.d. Poisson assumption
– goodness of fit tests
– graphical diagnostics
• Compare models using likelihoods and the
Bayes Information Criterion (BIC)
• Future versus past patterns of randomness
• Backtesting
16
Biologically reasonable and plausible forecasts
• Biologically reasonable
e.g. inverted mortality curve??
strong mean reversion??
time horizon matters
• Plausible forecasts
trend and degree of uncertainty
17
Robustness
• What happens if I change the age range?
• What happens if I add one extra calendar year?
• Revised parameter estimates and forecasts should be similar to
old
18
Not a black box
• Understand the advantages and disadvantages of each model
• Understand the limitations and assumptions of each model
• Better understanding of the model ⇒
– Better understanding of the risks
– Good risk management practice
19
Measures of mortality
• q(t, x) = underlying mortality rate in year t at age x
• m(t, x) = underlying death rate
• Assume q(t, x) = 1− exp[−m(t, x)]
Poisson model:
Exposures: E(t, x)
Actual deaths: D(t, x) ∼ Poisson (m(t, x)E(t, x))
in year t, age x last birthday
20
The Lee-Carter (1992) model
logm(t, x) = β(1)x + β(2)
x κt
Component 1: β(1)x
• Age effect
• Baseline log-mortality curve (κt = 0)
21
logm(t, x) = β(1)x + β(2)
x κt
Component 2: β(2)x κt
• Age-period component
• κt: period effect
– changes with time, t⇒ mortality improvements
• β(2)x : age effect
– dictates relative rates of improvement at different ages
22
The Lee-Carter (1992) model
logm(t, x) = β(1)x + β(2)
x κt
• Time series model for κt (e.g. random walk)
• Single κt for all ages
• Future T : St.Dev.[logm(T, x)] = β(2)x × St.Dev.[κT ]
23
Comparison of Eight Models
Cairns, et al (2009) North American Actuarial Journal
• 8 models
• Historical data
• Backtesting
• Plausibility of forecasts
24
General class of models
logm(t, x) = β(1)x κ
(1)t γ
(1)t−x + . . .+ β(N)
x κ(N)t γ
(N)t−x
OR
logit q(t, x) = β(1)x κ
(1)t γ
(1)t−x + . . .+ β(N)
x κ(N)t γ
(N)t−x
• β(k)x = age effect for component k
• κ(k)t = period effect for component k
• γ(k)t−x = cohort effect for component k
25
Lee-Carter family
logm(t, x) = β(1)x κ
(1)t γ
(1)t−x + . . .+ β(N)
x κ(N)t γ
(N)t−x
• β(k)x = non-parametric age effects
not smooth (can be smoothed)
• κ(k)t and γ
(k)t−x = random period and cohort effects
26
M1: Lee-Carter (1992) model (LC)
logm(t, x) = β(1)x + β(2)
x κ(2)t
• N = 2 components
• β(1)x , β(2)
x age effects
• κ(2)t single random period effect
• κ(1)t ≡ 1
• # parameters = 2× nages + nyears
27
Cohort Effects (e.g. Willetts, 2004)
Annual mortality improvement rates (Engl. & Wales, males)
1970 1980 1990 2000
2040
6080
Year
Age
−2%
−1%
0%
1%
2%
3%
4%
Ann
ual i
mpr
ovem
ent r
ate
(%)
28
M2: Renshaw-Haberman (2006) model (RH)
logm(t, x) = β(1)x + β(2)
x κ(2)t + β(3)
x γ(3)t−x
• N = 3 components
• β(1)x , β(2)
x , β(3)x age effects
• κ(2)t single random period effect
• γ(3)t−x single cohort effect
29
M3: Age-Period-Cohort model (APC)
logm(t, x) = β(1)x + κ
(2)t + γ
(3)t−x
• N = 3 components
• Special case of R-H model
• β(1)x age effect; β(2)
x = β(3)x = 1
• κ(2)t single random period effect
• γ(3)t−x single random cohort effect
30
Background
• M1: Lee-Carter
– First (??) stochastic mortality model
– Simple and robust
– Reasonable fit over a wide range of ages
• M2: Renshaw-Haberman
– Incorporation of a cohort effect
• M3: APC
– Roots in medical statistics, pre Lee-Carter
– Simpler and more robust than R-H
31
M4: P-splines family
Age-Period models
logm(t, x) =∑k,l
β(k)x κ
(l)t γ
(k,l)t−x
where
• β(k)x and κ
(l)t are B-spline basis functions
• γ(k,l)t−x are constant in t− x for each (k, l)
32
Background
• M4: Age-Cohort P-splines model
– Data are noisy
– Underlying m(t, x) is smooth
– Model ⇒ parsimonious, non-parametric fit
– Output: confidence intervals for underlying smooth surface
(Non-parametric generalisation of linear regression)
33
CBD family
logit q(t, x) = logq(t, x)
1− q(t, x)
= β(1)x κ
(1)t γ
(1)t−x + . . .+ β(N)
x κ(N)t γ
(N)t−x
• β(k)x = parametric age effects
pre-specified, e.g. constant, linear, quadratic in x
• κ(k)t and γ
(k)t−x = random period and cohort effects
34
M5: Cairns-Blake-Dowd (2006) model (CBD-1)
logit q(t, x) = κ(1)t + κ
(2)t (x− x) =
2∑i=1
β(i)x κ
(i)t γ
(i)t−x
• N = 2 components
• β(1)x = 1, β(2)
x = (x− x) age effects
• κ(1)t , κ
(2)t correlated random period effects
• γ(1)t−x = γ
(2)t−x ≡ 1 (model has no cohort effect)
35
Background
• M5: CBD-1
– Designed to take advantage of simple structure at higher ages
⇒ focus on pension plan longevity risk
– Two random period effects ⇒ allows different improvements at
different ages at different times
– Simple and robust, good at bigger picture
36
Case study: England and Wales males
60 65 70 75 80 85 90 95
−5
−4
−3
−2
−1
0
Age of cohort at the start of 2002
log
q_y/
(1−
q_y)
qy = mortality rate at age y in 2002
Data suggests logit qy = log qy/(1− qy) is linear
37
M6-M8: Cohort-effect extensions to CBD-1
• M6:logit q(t, x) = κ
(1)t + κ
(2)t (x− x) + γ
(3)t−x
• M7:logit q(t, x) = κ
(1)t + κ
(2)t (x− x)
+κ(3)t
{(x− x)2 − σ2
x
}+ γ
(4)t−x
• M8:logit q(t, x) = κ
(1)t + κ
(2)t (x− x) + γ
(3)t−x(xc − x)
38
Background
• M6-M8: CBD-2/3/4
– Developed during the course of the bigger study
– Build on the advantages of M1-M5
– Avoid the disadvantages of M1-M5
– Models focus on the higher ages
39
Past and Present: Modelling Genealogy
--Time
APC model (M3) - APC model (M3)
Booth et al.
Hyndman et al.
Lee-Carter (M1) -���
���
���
���3
@@@ -
Currie/Richards (M4)2-D P-splines
Eilers/MarxP-splines
��1-
DDE
���
���
-
CBD-1 (M5) -Q
QQsJJJJJ
JJJJJ - CBD-R
Mavros et al.
Renshaw-Haberman (M2)�����
CBD-2 (M6)
CBD-3 (M7)
CBD-4 (M8)
���*
��
���
-CBD-5 (M9)
Plat -���@@RAAAU
@@ ����- Multi-
population
- Multi-population
40
Quantitative Criteria
Bayes Information Criterion (BIC)
• Model k: lk = model maximum likelihood
• BIC penalises over-parametrised models
• Model k: BICk = lk − 12nk logN
– nk = number of parameters (effective)
– N = number of observations
41
Maximum Likelihood Estimation
Usual approach:• Stage 1: estimate the β(k)
x , κ(k)t , γ
(k)t−x without reference to the
stochastic models governing the period and cohort effects.
• Stage 2: fit a stochastic model to the κ(k)t and γ
(k)t−x
• Okay for large populations
• Smaller populations: exercise caution –β(k)x , κ
(k)t , γ
(k)t−x subject to estimation error
42
Alternatives to 2-stage MLE
• 1-stage MLE
– Models for κ(k)t , γ
(k)t−x specified in advance
• Full Bayesian model (e.g. Czado et al.)
– Models for κ(k)t , γ
(k)t−x specified in advance
– Output includes posterior distributions for model parameters
plus latent β(k)x , κ
(k)t , γ
(k)t−x
43
2-Stage MLE: Application to 8 Models
• England and Wales males
• 1961-2004
• Ages 60-89
• Exclusions
– 1961-1970: ages 85-89 (not available)
– 1886 cohort (unreliable exposures)
– Cohorts with 4 or fewer data points (overfitting)
44
Typical parameter estimation results: M3-APC
60 65 70 75 80 85 90
−4.
5−
4.0
−3.
5−
3.0
−2.
5−
2.0
−1.
5−
1.0
Age Effect, beta1
Age1960 1970 1980 1990 2000
−15
−10
−5
05
1015
Period Effect, Kappa2
Year1880 1900 1920 1940
−10
−8
−6
−4
−2
02
4
Cohort Effect, Gamma3
Year of Birth
45
Model Max log-lik. # parameters BIC (rank)
M1: LC -8912.7 102 -9275.8
M2: RH -7735.6 203 -8458.1
M3: APC -8608.1 144 -9120.6
M4: P-Splines -9245.9 74.2 -9372.9
M5: CBD-1 -10035.5 88 -10348.8
M6: CBD-2 -7922.3 159 -8488.3
M7: CBD-3 -7702.1 202 -8421.1
M8: CBD-4 -7823.7 161 -8396.8
46
The BIC doesn’t tell us the whole story ...
Qualitative Criteria – Graphical diagnostics
• Poisson model ⇒ (t, x) cells are all independent.
• Standardised residuals:
Z(t, x) =D(t, x)− m(t, x)E(t, x)√
m(t, x)E(t, x)
• If the data are not i.i.d.:
What do the patterns tell us?
47
Are standardised residuals i.i.d.? LC and RH models
1970 1980 1990 2000
6065
7075
8085
90
Model M1
1970 1980 1990 2000
6065
7075
8085
90
Model M2
Black ⇒ Z(t, x) < 0
48
APC and P-splines models
1970 1980 1990 2000
6065
7075
8085
90
Model M3
1970 1980 1990 2000
6065
7075
8085
90
Model M4
49
CBD-1 and CBD-2 models
1970 1980 1990 2000
6065
7075
8085
90
Model M5
1970 1980 1990 2000
6065
7075
8085
90
Model M6
50
CBD-3 and CBD-4 models
1970 1980 1990 2000
6065
7075
8085
90
Model M7
1970 1980 1990 2000
6065
7075
8085
90
Model M8
51
Are the standardised residuals i.i.d.?
More graphical diagnostics:
Scatterplots of residuals versus
• Age
• Year of observation
• Year of birth
52
M1: LC model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
53
M2: RH model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
54
M3: APC model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
55
M4: P-splines model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
56
M5: CBD-1 model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
57
M6: CBD-2 model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
58
M7: CBD-3 model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
59
M8: CBD-4 model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
60
Robustness
Want to see stability in parameter estimates
• Extra years of data
• Extra ages
• Within model hierarchy
61
M7 (CBD-3): (a) 1961 to 2004 (dots) or (b) 1981 to 2004 (solid lines).
1970 1980 1990 2000
−3.
2−
2.8
−2.
4 Kappa_1(t)
Year
1970 1980 1990 2000
0.08
00.
090
0.10
00.
110 Kappa_2(t)
Year
1970 1980 1990 2000−1
e−
03−
4 e
−04
2 e
−04
Kappa_3(t)
Year
1880 1900 1920 1940
−0.
060.
000.
040.
08
Gamma_4(t−x)
Year of birth
62
RECAP: M5: CBD-1 model
1960 1980 2000
−10
−5
05
10
Year of Observation
Sta
ndar
dise
d re
sidu
als
60 70 80 90
−10
−5
05
10
Age
Sta
ndar
dise
d re
sidu
als
1880 1910 1940
−10
−5
05
10
Year of Birth
Sta
ndar
dise
d re
sidu
als
63
M2 (RH): (a) 1961 to 2004 (dots) or (b) 1981 to 2004 (solid lines).
60 65 70 75 80 85 90
−4.
0−
3.0
−2.
0−
1.0 Beta_1(x)
Age
60 65 70 75 80 85 90
0.01
0.02
0.03
0.04
0.05
0.06
Beta_2(x)
Age
1970 1980 1990 2000
−10
−5
05
10
Kappa_2(t)
Year
60 65 70 75 80 85 90
−0.
10.
00.
10.
20.
30.
4
Beta_3(x)
Age
1880 1900 1920 1940
−10
−5
05
Gamma_3(t−x)
Year of birth
64
Robustness
• Parameter estimates should not be too sensitive to the choice of
range of ages and years.
• M2 has a possible problem
• β(3)x age effect seems to be qualitatively different for the
1961-2004 versus 1981-2004
65
Qualitative criteria or issues
• Forecast reasonableness
• More on robustness
66
Simulation models
• Up to now: historical fit only
• Forecasting requires a stochastic model
• ARIMA time series models to simulate future period and cohort
effects
⇒ Process Risk or Stochastic Risk
• Later: parameter risk and model risk
67
Simulation models
Examples:
• M1: Lee-Carter model
– period effect, κ(2)t = random walk with drift
• M7: CBD-3 model
– (κ(1)t , κ
(2)t , κ
(3)t ) = multivariate random walk with drift
– γ(4)c = AR(1) model ≡ARIMA(1,0,0)
68
Mortality Fan Charts + A plausible set of forecasts
1960 1980 2000 2020 2040
0.00
50.
010
0.02
00.
050
0.10
00.
200
AGE 65
AGE 75
AGE 85
Year, t
Mor
talit
y ra
te, q
(t,x
)
Model CBD−1 Fan Chart
69
Model risk
1960 1980 2000 2020 2040
0.00
50.
010
0.02
00.
050
0.10
00.
200
AGE 65
AGE 75
AGE 85
Year, t
Mor
talit
y ra
te, q
(t,x
)
Model CBD−1 Fan Chart
70
Model risk
1960 1980 2000 2020 2040
0.00
50.
010
0.02
00.
050
0.10
00.
200
AGE 65
AGE 75
AGE 85
Year, t
Mor
talit
y ra
te, q
(t,x
)
Combined CBD−1, CBD−3 Fan Chart
71
Model risk
1960 1980 2000 2020 2040
0.00
50.
010
0.02
00.
050
0.10
00.
200
AGE 65
AGE 75
AGE 85
Year, t
Mor
talit
y ra
te, q
(t,x
)
Combined CBD−1, CBD−3, CBD−4 Fan Chart
72
Plausibility of forecasts
• Defining “Plausible” is impossible!
• Visually: given the forecast
– are you reasonably comfortable?
– slightly uncomfortable?
– fan chart is clearly unreasonable?
73
US males 1968-2003: M8 – unreasonable forecasts
1960 1980 2000 2020 2040
0.00
20.
005
0.02
00.
050
0.20
0
x = 84
x = 75
x=65
Mor
talit
y R
ate
M8A
74
Robustness of Forecasts
• Forecasts Set 1:
– Data from 1961-2004 ⇒ β(k)x , κ
(k)t , γ
(k)t−x
– Use full set of β(k)x , κ
(k)t , γ
(k)t−x to make forecasts
• Forecasts Set 3:
• Data from 1981-2004 ⇒ β(k)x , κ
(k)t , γ
(k)t−x
• Use full set of β(k)x (30), κ
(k)t (24), γ
(k)t−x (45) to make forecasts
75
Robustness of Forecasts
• Forecasts Set 2:
– Data from 1961-2004 ⇒ β(k)x , κ
(k)t , γ
(k)t−x
– To make forecasts:
∗ Use all 30 β(k)x
∗ Use the last 24 κ(k)t only (out of 44)
∗ Use the last 45 γ(k)t−x only (out of 65)
– i.e. as if 1981-2004
76
Perfect model + large population
Forecast sets 2 and 3:
• Same β(i)x , κ
(i)t , γ
(i)t−x
• Same forecasts
Good robust model
Forecast sets 2 and 3:
• Similar β(i)x , κ
(i)t , γ
(i)t−x
• Similar forecasts
77
Robustness: e.g. M3 - Age-Period-Cohort model
1960 1980 2000 2020 2040
0.01
0.02
0.05
0.10
1961−2004 data: APC full1961−2004 data: APC limited1981−2004 data: APC
APC Model − Age 75 Mortality Rates
Year, t
Mor
talit
y ra
te
78
Robustness: e.g. M7 - CBD-3 model
1900 1940 1980
−0.
10−
0.05
0.00
0.05
0.10
Model M7
gam
ma4
1960 1980 2000 2020 2040
0.01
0.02
0.04
Age 65 Mortality Rates
Mor
talit
y ra
te
1960 1980 2000 2020 2040
0.01
0.02
0.05
0.10
Age 75 Mortality Rates
Mor
talit
y ra
te
1960 1980 2000 2020 2040
0.05
0.10
0.20
Age 85 Mortality Rates
Mor
talit
y ra
te
79
Not all models are robust: Renshaw-Haberman model
1960 1980 2000 2020 2040
0.00
50.
020
0.05
00.
200
1961−2004 data: R−H full1961−2004 data: R−H limited1981−2004 data: R−H
Model R−H (ARIMA(1,1,0)) projections
Year, t
Coh
ort e
ffect
x=65
x=75
x=85
80
Robustness Problem
• Likely reason: Likelihood function has multiple maxima
• Consequences:
– Lack of robustness within sample
– Lack of robustness in forecasts
∗ central trajectory
∗ prediction intervals
– Some sample periods ⇒ implausible forecasts
81
Parameter Uncertainty: CBD model M5 example
1960 1970 1980 1990 2000
−3.
4−
3.0
−2.
6−
2.2
2−factor model: Kappa_1(t)=1
Year, t
1960 1970 1980 1990 2000
0.08
50.
095
0.10
5
2−factor model: Kappa_2(t)
Year, t
82
κt = (κ(1)t , κ
(2)t )′
Model: Random walk with drift
κt+1 − κt = µ+ CZ(t+ 1)
• µ = (µ1, µ2)′ = drift
• V = CC ′ = variance-covariance matrix
• Estimate µ and V
• Quantify parameter uncertainty in µ and V
• Quantify the impact of parameter uncertainty
83
Application: cohort survivorship
• Cohort: Age x at time t = 0
• S(t, x) = survivor index at t
proportion surviving from time 0 to time t
S(t, x) = (1− q(0, x))× (1− q(1, x+ 1))× . . .
. . .× (1− q(t− 1, x+ t− 1))
84
90% Confidence Interval (CI) for Cohort Survivorship
65 70 75 80 85 90
0.0
0.2
0.4
0.6
0.8
1.0 Data from 1982−2002
Age
Pro
port
ion
surv
ivin
g, S
(x)
E[S(x)] with param. uncertaintyCI without param. uncertaintyCI with param. uncertainty
85
Cohort Survivorship: General Conclusions
• Less than 10 years:
– Systematic risk not significant
• Over 10 years
– Systematic risk becomes more and more significant over time
• Over 20 years
– Parameter risk begins to dominate (+ model risk)
86
Part 1: Concluding remarks
• Range of models to choose from
• Quantitative criteria are only the starting point
• Additional criteria ⇒
– Some models pass
– Some models fail
• Focus here on mortality data at higher ages
– Wider age range ⇒ CBD models less good
87
Applications – A Taster
88
Applications – Scenario Generation
Example: the Lee Carter Model
• m(t, x) = β(1)(x) + β(2)(x)κ(t)
• Choose a time series model for κ(t)
• Calibrate the time series parameters using data up to the current
time (time 0)
• Generate j = 1, . . . , N stochastic scenarios of κ(t)
κ1(t), . . . , κN(t)
89
• Generate N scenarios for the future m(t, x)
mj(t, x) for j = 1, . . . , N , t = 0, 1, 2, . . ., x = x0, . . . , x1
• Generate N scenarios for the survivor index, Sj(t, x)
• Calculate financial functions
+ variations for some financial applications.
90
0 10 20 30 40 50 60
−1.
0−
0.5
0.0
0.5
Historical Simulated
Period Effect: One Scenario
Time
Per
iod
Effe
ct, k
appa
(t)
κ(t): Generate scenario 1
91
0 10 20 30 40 50 60
−1.
0−
0.5
0.0
0.5
Historical Simulated
Period Effect: Multiple Scenarios
Time
Per
iod
Effe
ct, k
appa
(t)
Multiple scenarios
92
0 10 20 30 40 50 60
−1.
0−
0.5
0.0
0.5
Historical Simulated
Period Effect: Fan Chart
Time
Per
iod
Effe
ct, k
appa
(t)
Fan chart
93
0 5 10 15 20 25 30
0.00
60.
008
0.01
20.
016
Death Rates, Age 65: One Scenario
Time
Dea
th R
ate
(log
scal
e)
94
0 5 10 15 20 25 30
0.00
60.
008
0.01
20.
016
Death Rates, Age 65: Multiple Scenarios
Time
Dea
th R
ate
(log
scal
e)
95
0 5 10 15 20 25 30
0.00
60.
008
0.01
20.
016
Death Rates, Age 65: Fan Chart
Time
Dea
th R
ate
(log
scal
e)
96
30 35 40 45 50 55 60
6570
7580
8590
Extract Cohort Death Rates, m(t,x+t−1)
Time
Age
Annuity valuation ⇒ follow cohorts m(0, x) → m(1, x+ 1) → m(2, x+ 2) . . .
97
65 70 75 80 85 90 95 100
0.01
0.02
0.05
0.10
0.20
Cohort Death Rates From Age 65:One Scenario
Cohort Age
Dea
th R
ate
(log
scal
e)
Annuity valuation ⇒ follow cohorts m(0, x) → m(1, x+ 1) → m(2, x+ 2) . . .
98
65 70 75 80 85 90 95 100
0.01
0.02
0.05
0.10
0.20
Cohort Death Rates From Age 65:Multpiple Scenarios
Cohort Age
Dea
th R
ate
(log
scal
e)
99
65 70 75 80 85 90 95 100
0.01
0.02
0.05
0.10
0.20
Cohort Death Rates From Age 65:Fan Chart
Cohort Age
Dea
th R
ate
(log
scal
e)
100
65 70 75 80 85 90 95 100
0.0
0.2
0.4
0.6
0.8
1.0
Survivorship From Age 65:One Scenario
Cohort Age
Sur
vivo
r In
dex
(log
scal
e)
Cohort death rates −→ cohort survivorship
101
65 70 75 80 85 90 95 100
0.0
0.2
0.4
0.6
0.8
1.0
Survivorship From Age 65:Multiple Scenarios
Cohort Age
Sur
vivo
r In
dex
(log
scal
e)
102
65 70 75 80 85 90 95 100
0.0
0.2
0.4
0.6
0.8
1.0
Survivorship From Age 65:Fan Chart
Cohort Age
Sur
vivo
r In
dex
(log
scal
e)
103
17 18 19 20 21 22
010
020
030
040
050
0Cohort Life Expectancy
from Age 65
Life Expectancy From Age 65
Fre
quen
cy
17 18 19 20 21 22
0.0
0.2
0.4
0.6
0.8
1.0
Cohort Life Expectancy from Age 65
Life Expectancy From Age 65
Cum
ulat
ive
Pro
babi
lity
Cohort survivorship −→ ex post cohort life expectancy
Equivalent to a continuous annuity with 0% interest
104
14 15 16 17
020
040
060
080
0Present Value of Annuity
from Age 65
Present Value ofAnnuity From Age 65
Fre
quen
cy
13.5 14.5 15.5 16.5
0.0
0.2
0.4
0.6
0.8
1.0
Present Value of Annuity from Age 65
Present Value ofAnnuity From Age 65
Cum
ulat
ive
Pro
babi
lity
• Annuity of 1 per annum payable annual in arrears
• Interest rate: 2%
105
14 15 16 17
020
040
060
080
0
Mean 99.5% VaR+8%
Present Value of Annuity from Age 65
Present Value ofAnnuity From Age 65
Fre
quen
cy
13.5 14.5 15.5 16.5
0.0
0.2
0.4
0.6
0.8
1.0
Present Value of Annuity from Age 65
Present Value ofAnnuity From Age 65
Cum
ulat
ive
Pro
babi
lity
• Mean = $15.17 per $1 annuity; BUT
• Need $16.38 to be 99.5% sure of covering all liabilities
106
30 35 40 45 50 55 60
6570
7580
8590
Extract Period Death Rates, m(t,x+t−1)
Time
Age
Period life expectancy and related quantities
107
0 10 20 30 40 50 60
1416
1820
2224
Historical Forecast
Period Life Expectancy From Age 65By Calendar Year
Time
Per
iod
Life
Exp
ecta
ncy
108
0 10 20 30 40 50 60
0.00
50.
010
0.01
50.
020
Historical SimulatedRecalibration+
Central Forecast
Simulated
Recalibration
Death Rates, Age 65: One Scenario
Time
Dea
th R
ate
(log
scal
e)
• Valuation at time 10 ⇒
• Recalibrate model and parameters → central forecast
• Updated liability value at time 10
109
0 10 20 30 40 50 60
0.00
50.
010
0.01
50.
020
Historical SimulatedRecalibration+
Central ForecastR
ecalibration
Death Rates, Age 65: Multiple Scenarios
Time
Dea
th R
ate
(log
scal
e)
110
13.5 14.0 14.5 15.0 15.5 16.0 16.5
0.0
0.2
0.4
0.6
0.8
1.0
PV: Full RunoffPV: Valuation at Time 10PV: Valuation at Time 0
Present Value of Annuity from Age 65
Present Value ofAnnuity From Age 65
Cum
ulat
ive
Pro
babi
lity
Applications: Hedging longevity risk
111
Part 2: Concluding Remarks
• Here: Lee-Carter →m(t, x)→ application
• Modular code ⇒Model X →m(t, x); m(t, x)→ application
• Applications
– Development of simple stress tests
– Reserving
– Longevity risk transfer
• Multi-population models
112
References
• Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., and Balevich, I. (2009)
A quantitative comparison of stochastic mortality models using data from England and Wales and the United States.
North American Actuarial Journal 13(1): 1-35.
• Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G., Epstein, D., and Khalaf-Allah M. (2011)
The Plausibility of Mortality Density Forecasts: An Analysis of Six Stochastic Mortality Models.
Insurance: Mathematics and Economics, 48: 355-367.
• Cairns, A.J.G., Kallestrup-Lamb, M., Rosenskjold, C.P.T., Blake, D., and Dowd, K., (2017)
Modelling Socio-Economic Differences in the Mortality of Danish Males Using a New Affluence Index.
Preprint. http://www.macs.hw.ac.uk/∼andrewc/papers/ajgc73.pdf