4. This Edition 2002
Ó The Faculty of Actuaries and The Institute of Actuaries
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ISBN 0 901066 57 5
5. PREFACE
This new edition of the Formulae and Tables represents a considerable overhaul
of its predecessor “green book” first published in 1980.
The contents have been updated to reflect more fully the evolving syllabus
requirements of the profession, and also in the case of the Tables to reflect more
contemporary experience and methods. Correspondingly, there has been some
modest removal of material which has either become redundant with syllabus
changes or obviated by the availability of pocket calculators.
As in the predecessor book, it is important to note that these tables have been
produced for the sole use of examination candidates. The profession does not
express any opinion whatsoever as to the circumstances in which any of the
tables may be suitable for other uses.
6.
7. 1
FORMULAE
This section is intended to help candidates with formulae that may be hard to
remember. Derivations of these formulae may still be required under the
relevant syllabuses.
Contents Page
Mathematical Methods 2
Statistical Distributions 6
Statistical Methods 22
Compound Interest 31
Survival Models 32
Annuities and Assurances 36
Stochastic Processes 38
Time Series 40
Economic Models 43
Financial Derivatives 45
Note. In these tables, log denotes logarithms to base e.
8. 2
1 MATHEMATICAL METHODS
1.1 SERIES
Exponential function
2 3
exp( ) 1
2! 3!
x x x
x e x= = + + + +L
Natural log function
2 3
log(1 ) ln(1 )
2 3
x x
x x x+ = + = - + -L ( 1 1x- < £ )
Binomial expansion
1 2 2
( )
1 2
n n n n nn n
a b a a b a b b- -æ ö æ ö
+ = + + + +ç ÷ ç ÷è ø è ø
L
where n is a positive integer
2 3( 1) ( 1)( 2)
(1 ) 1
2! 3!
( 1 1)
p p p p p p
x px x x
x
- - -
+ = + + + +
- < <
L
9. 3
1.2 CALCULUS
Taylor series (one variable)
2
( ) ( ) ( ) ( )
2!
h
f x h f x hf x f x+ = + + +¢ ¢¢ L
Taylor series (two variables)
( )2 2
( , ) ( , ) ( , ) ( , )
1
( , ) 2 ( , ) ( , )
2!
x y
xx xy yy
f x h y k f x y h f x y k f x y
h f x y hk f x y k f x y
+ + = + +¢ ¢
+ + + +¢¢ ¢¢ ¢¢ L
Integration by parts
[ ]
b bb
aa a
dv du
u dx uv v dx
dx dx
= -ò ò
Double integrals (changing the order of integration)
( , ) ( , )
b x b b
a a a y
f x y dy dx f x y dx dyæ ö æ ö=
è ø è øò ò ò ò or
( , ) ( , )
b x b b
a a a y
dx dy f x y dy dx f x y=ò ò ò ò
The domain of integration here is the set of values ( , )x y for which
a y x b£ £ £ .
Differentiating an integral
( )
( )
( , ) ( ) [ ( ), ] ( ) [ ( ), ]
b y
a y
d
f x y dx b y f b y y a y f a y y
dy
= -¢ ¢ò
( )
( )
( , )
b y
a y
f x y dx
y
¶
+
¶ò
10. 4
1.3 SOLVING EQUATIONS
Newton-Raphson method
If x is a sufficiently good approximation to a root of the equation
( ) 0f x = then (provided convergence occurs) a better approximation
is
( )
*
( )
f x
x x
f x
= -
¢
.
Integrating factors
The integrating factor for solving the differential equation
( ) ( )
dy
P x y Q x
dx
+ = is:
( )exp ( )P x dxò
Second-order difference equations
The general solution of the difference equation
2 1 0n n nax bx cx+ ++ + = is:
if 2
4 0b ac- > : 1 2
n n
nx A B= l + l
(distinct real roots, 1 2l ¹ l )
if 2
4 0b ac- = : ( ) n
nx A Bn= + l
(equal real roots, 1 2l = l = l )
if 2
4 0b ac- < : ( cos sin )n
nx r A n B n= q + q
(complex roots, 1 2
i
re q
l = l = )
where 1l and 2l are the roots of the quadratic equation
2
0a b cl + l + = .
11. 5
1.4 GAMMA FUNCTION
Definition
1
0
( ) x t
x t e dt
¥
- -
G = ò , 0x >
Properties
( ) ( 1) ( 1)x x xG = - G -
( ) ( 1)!n nG = - , 1,2,3,n = K
(½)G = p
1.5 BAYES’ FORMULA
Let 1 2, ,..., nA A A be a collection of mutually exclusive and exhaustive
events with ( ) 0,iP A ¹ i = 1, 2, …, n.
For any event B such that P(B) ¹ 0:
1
( | ) ( )
( | ) , 1, 2,..., .
( | ) ( )
i i
i n
j j
j
P B A P A
P A B i n
P B A P A
=
= =
å
12. 6
2 STATISTICAL DISTRIBUTIONS
Notation
PF = Probability function, ( )p x
PDF = Probability density function, ( )f x
DF = Distribution function, ( )F x
PGF = Probability generating function, ( )G s
MGF = Moment generating function, ( )M t
Note. Where formulae have been omitted below, this indicates that
(a) there is no simple formula or (b) the function does not have a
finite value or (c) the function equals zero.
2.1 DISCRETE DISTRIBUTIONS
Binomial distribution
Parameters: n , p (n = positive integer, 0 1p< < with 1q p= - )
PF: ( ) x n xn
p x p q
x
-æ ö
= ç ÷è ø
, 0,1,2, ,x n= K
DF: The distribution function is tabulated in the statistical
tables section.
PGF: ( ) ( )n
G s q ps= +
MGF: ( ) ( )t n
M t q pe= +
Moments: ( )E X np= , var( )X npq=
Coefficient
of skewness:
q p
npq
-
13. 7
Bernoulli distribution
The Bernoulli distribution is the same as the binomial distribution
with parameter 1n = .
Poisson distribution
Parameter: m ( 0m > )
PF: ( )
!
x
e
p x
x
-m
m
= , 0,1,2,...x =
DF: The distribution function is tabulated in the statistical
tables section.
PGF: ( 1)
( ) s
G s em -
=
MGF: ( 1)
( )
t
e
M t em -
=
Moments: ( )E X = m , var( )X = m
Coefficient
of skewness:
1
m
14. 8
Negative binomial distribution – Type 1
Parameters: k , p (k =positive integer, 0 1p< < with 1q p= - )
PF:
1
( )
1
k x kx
p x p q
k
--æ ö
= ç ÷-è ø
, , 1, 2,x k k k= + + K
PGF: ( )
1
k
ps
G s
qs
æ ö
= ç ÷è ø-
MGF: ( )
1
kt
t
pe
M t
qe
æ ö
= ç ÷
-è ø
Moments: ( )
k
E X
p
= , 2
var( )
kq
X
p
=
Coefficient
of skewness:
2 p
kq
-
15. 9
Negative binomial distribution – Type 2
Parameters: k , p ( 0k > , 0 1p< < with 1q p= - )
PF:
( )
( )
( 1) ( )
k xk x
p x p q
x k
G +
=
G + G
, 0,1,2,x = K
PGF: ( )
1
k
p
G s
qs
æ ö
= ç ÷è ø-
MGF: ( )
1
k
t
p
M t
qe
æ ö
= ç ÷
è ø-
Moments: ( )
kq
E X
p
= , 2
var( )
kq
X
p
=
Coefficient
of skewness:
2 p
kq
-
Geometric distribution
The geometric distribution is the same as the negative binomial
distribution with parameter 1k = .
16. 10
Uniform distribution (discrete)
Parameters: a , b, h (a b< , 0h > , b a- is a multiple of h )
PF: ( )
h
p x
b a h
=
- +
, , , 2 , , ,x a a h a h b h b= + + -K
PGF: ( )
1
b h a
h
h s s
G s
b a h s
+æ ö-
= ç ÷- + -è ø
MGF:
( )
( )
1
b h t at
ht
h e e
M t
b a h e
+æ ö-
= ç ÷- + -è ø
Moments:
1
( ) ( )
2
E X a b= + ,
1
var( ) ( )( 2 )
12
X b a b a h= - - +
2.2 CONTINUOUS DISTRIBUTIONS
Standard normal distribution – (0,1)N
Parameters: none
PDF:
21
2
1
( )
2
x
f x e
-
=
p
, x-¥ < < ¥
DF: The distribution function is tabulated in the statistical
tables section.
MGF:
21
2( )
t
M t e=
Moments: ( ) 0E X = , var( ) 1X =
2
1 (1 )
( )
2 1
2
r
r
r
E X
r
G +
=
æ ö
G +ç ÷è ø
, 2,4,6,r = K
17. 11
Normal (Gaussian) distribution – 2
( , )N m s
Parameters: m , 2
s ( 0s > )
PDF:
2
1 1
( ) exp
22
x
f x
ì ü- mï ïæ ö
= -í ýç ÷è øss p ï ïî þ
, x-¥ < < ¥
MGF:
2 21
2( )
t t
M t e
m + s
=
Moments: ( )E X = m , 2
var( )X = s
Exponential distribution
Parameter: l ( 0l > )
PDF: ( ) x
f x e-l
= l , 0x >
DF: ( ) 1 x
F x e-l
= -
MGF:
1
( ) 1
t
M t
-
æ ö
= -ç ÷è øl
, t < l
Moments:
1
( )E X =
l
, 2
1
var( )X =
l
(1 )
( )r
r
r
E X
G +
=
l
, 1,2,3,r = K
Coefficient
of skewness: 2
18. 12
Gamma distribution
Parameters: a , l ( 0a > , 0l > )
PDF: 1
( )
( )
x
f x x e
a
a- -ll
=
G a
, 0x >
DF: When 2a is an integer, probabilities for the gamma
distribution can be found using the relationship:
2
22 ~X al c
MGF: ( ) 1
t
M t
-a
æ ö
= -ç ÷è øl
, t < l
Moments: ( )E X
a
=
l
, 2
var( )X
a
=
l
( )
( )
( )
r
r
r
E X
G a +
=
G a l
, 1,2,3,r = K
Coefficient
of skewness:
2
a
Chi-square distribution – u
2
c
The chi-square distribution with u degrees of freedom is the same as
the gamma distribution with parameters
2
u
a = and
1
2
l = .
The distribution function for the chi-square distribution is tabulated in
the statistical tables section.
19. 13
Uniform distribution (continuous) – U(a, b)
Parameters: ,a b (a b< )
PDF:
1
( )f x
b a
=
-
, a x b< <
DF: ( )
x a
F x
b a
-
=
-
MGF:
1 1
( ) ( )
( )
bt at
M t e e
b a t
= -
-
Moments:
1
( ) ( )
2
E X a b= + , 21
var( ) ( )
12
X b a= -
1 11 1
( ) ( )
( ) 1
r r r
E X b a
b a r
+ +
= -
- +
, 1,2,3,r = K
Beta distribution
Parameters: a , b ( 0a > , 0b > )
PDF: 1 1( )
( ) (1 )
( ) ( )
f x x xa- b-G a + b
= -
G a G b
, 0 1x< <
Moments: ( )E X
a
=
a + b
, 2
var( )
( ) ( 1)
X
ab
=
a + b a + b +
( ) ( )
( )
( ) ( )
r r
E X
r
G a + b G a +
=
G a G a + b +
, 1,2,3,r = K
Coefficient
of skewness:
2( ) 1
( 2)
b - a a + b +
a + b + ab
20. 14
Lognormal distribution
Parameters: m , 2
s ( 0s > )
PDF:
2
1 1 1 log
( ) exp
22
x
f x
x
ì ü- mï ïæ ö
= -í ýç ÷è øss p ï ïî þ
, 0x >
Moments:
21
2( )E X e
m+ s
= , )2 2
2
var( ) 1X e em+s sæ= -
è
2 21
2( )
r rr
E X e
m+ s
= , 1,2,3,r = K
Coefficient
of skewness: )2 2
2 1e es sæ + -
è
Pareto distribution (two parameter version)
Parameters: a , l ( 0a > , 0l > )
PDF: 1
( )
( )
f x
x
a
a+
al
=
l +
, 0x >
DF: ( ) 1F x
x
a
læ ö
= - ç ÷è øl +
Moments: ( )
1
E X
l
=
a -
( 1)a > ,
2
2
var( )
( 1) ( 2)
X
al
=
a - a -
( 2)a >
( ) (1 )
( )
( )
r rr r
E X
G a - G +
= l
G a
, 1,2,3,r = K, r < a
Coefficient
of skewness:
2( 1) 2
( 3)
a + a -
a - a
( 3)a >
21. 15
Pareto distribution (three parameter version)
Parameters: a , l , k ( 0a > , 0l > , 0k > )
PDF:
1
( )
( )
( ) ( )( )
k
k
k x
f x
k x
a -
a+
G a + l
=
G a G l +
, 0x >
Moments: ( )
1
k
E X
l
=
a -
( 1a > ),
2
2
( 1)
var( )
( 1) ( 2)
k k
X
+ a - l
=
a - a -
( 2a > )
( ) ( )
( )
( ) ( )
r rr k r
E X
k
G a - G +
= l
G a G
, 1,2,3,r = K, r < a
Weibull distribution
Parameters: c , g ( 0c > , 0g > )
PDF: 1
( ) cx
f x c x e
g
g - -
= g , 0x >
DF: ( ) 1 cx
F x e
g
-
= -
Moments:
1
( ) 1r
r
r
E X
c g
æ ö
= G +ç ÷è øg
, 1,2,3,r = K
Burr distribution
Parameters: a , l , g ( 0a > , 0l > , 0g > )
PDF:
1
1
( )
( )
x
f x
x
a g -
g a+
ag l
=
l +
, 0x >
DF: ( ) 1F x
x
a
g
læ ö
= - ç ÷è øl +
Moments: ( ) 1
( )
r
r r r
E X
g
æ ö æ ö l
= G a - G +ç ÷ ç ÷è ø è øg g G a
, 1,2,3,r = K,r < ag
22. 16
2.3 COMPOUND DISTRIBUTIONS
Conditional expectation and variance
( ) [ ( | )]E Y E E Y X=
var( ) var[ ( | )] [var( | )]Y E Y X E Y X= +
Moments of a compound distribution
If 1 2, ,X X K are IID random variables with MGF ( )XM t and N is
an independent nonnegative integer-valued random variable, then
1 NS X X= + +L (with 0S = when 0N = ) has the following
properties:
Mean: ( ) ( ) ( )E S E N E X=
Variance: 2
var( ) ( )var( ) var( )[ ( )]S E N X N E X= +
MGF: ( ) [log ( )]S N XM t M M t=
Compound Poisson distribution
Mean: 1ml
Variance: 2ml
Third central moment: 3ml
where ( )E Nl = and ( )r
rm E X=
23. 17
Recursive formulae for integer-valued distributions
( , ,0)a b class of distributions
Let ( )rg P S r= = , 0,1,2,r = K and ( )jf P X j= = , 1,2,3,j = K.
If ( )rp P N r= = , where 1r r
b
p a p
r
-
æ ö
= +ç ÷è ø
, 1,2,3,r = K, then
0 0g p= and
1
r
r j r j
j
bj
g a f g
r
-
=
æ ö
= +ç ÷è øå , 1,2,3,r = K
Compound Poisson distribution
If N has a Poisson distribution with mean l , then 0a = and b = l ,
and
0g e-l
= and
1
r
r j r j
j
g j f g
r
-
=
l
= å , 1,2,3,r = K
24. 18
2.4 TRUNCATED MOMENTS
Normal distribution
If ( )f x is the PDF of the 2
( , )N m s distribution, then
( ) [ ( ) ( )] [ ( ) ( )]
U
L
x f x dx U L U L= m F - F - s f - f¢ ¢ ¢ ¢ò
where
L
L
- m
=¢
s
and
U
U
- m
=¢
s
.
Lognormal distribution
If ( )f x is the PDF of the lognormal distribution with parameters m
and 2
s , then
2 2
½
( ) [ ( ) ( )]
U k k k
k kL
x f x dx e U Lm+ s
= F - Fò
where
log
k
L
L k
- m
= - s
s
and
log
k
U
U k
- m
= - s
s
.
28. 22
3 STATISTICAL METHODS
3.1 SAMPLE MEAN AND VARIANCE
The random sample 1 2( , , , )nx x xK has the following sample
moments:
Sample mean:
1
1 n
i
i
x x
n =
= å
Sample variance: 2 2 2
1
1
1
n
i
i
s x nx
n =
ì üï ï
= -í ý
- ï ïî þ
å
3.2 PARAMETRIC INFERENCE (NORMAL MODEL)
One sample
For a single sample of size n under the normal model 2
~ ( , )X N m s :
1~ n
X
t
S n
-
- m
and
2
2
12
( 1)
~ n
n S
-
-
c
s
Two samples
For two independent samples of sizes m and n under the normal
models 2
~ ( , )X XX N m s and 2
~ ( , )Y YY N m s :
2 2
1, 12 2
~X X
m n
Y Y
S
F
S
- -
s
s
29. 23
Under the additional assumption that 2 2
X Ys = s :
2
( ) ( )
~
1 1
X Y
m n
p
X Y
t
S
m n
+ -
- - m - m
+
where { }2 2 21
( 1) ( 1)
2p X YS m S n S
m n
= - + -
+ -
is the pooled sample
variance.
3.3 MAXIMUM LIKELIHOOD ESTIMATORS
Asymptotic distribution
If ˆq is the maximum likelihood estimator of a parameter q based on
a sample X , then ˆq is asymptotically normally distributed with mean
q and variance equal to the Cramér-Rao lower bound
2
2
( ) 1 log ( , )CRLB E L X
é ù¶
q = - qê ú
¶qê úë û
Likelihood ratio test
0
0 1
2
max
2( ) 2log ~
max
H
p p q q
H H
L
L
+
È
æ ö
ç ÷- - = - c
ç ÷
è ø
l l approximately (under 0H )
where
0
max logp
H
L=l is the maximum log-likelihood for the
model under 0H (in which there are
p free parameters)
and
0 1
max logp q
H H
L+
È
=l is the maximum log-likelihood for the
model under 0 1H HÈ (in which there
are p q+ free parameters).
30. 24
3.4 LINEAR REGRESSION MODEL WITH NORMAL ERRORS
Model
2
~ ( , )i iY N xa + b s , 1,2, ,i n= K
Intermediate calculations
2 2 2
1 1
( )
n n
xx i i
i i
s x x x nx
= =
= - = -å å
2 2 2
1 1
( )
n n
yy i i
i i
s y y y ny
= =
= - = -å å
1 1
( )( )
n n
xy i i i i
i i
s x x y y x y n x y
= =
= - - = -å å
Parameter estimates
ˆˆ y xa = - b , ˆ xy
xx
s
s
b =
2
2 2
1
1 1
ˆ ˆ( )
2 2
n
xy
i i yy
xxi
s
y y s
n n s=
æ ö
s = - = -ç ÷
- - è ø
å
Distribution of ˆb
2
2
ˆ
~
ˆ
n
xx
t
s
-
b - b
s
31. 25
Variance of predicted mean response
2
20
0
( )1ˆˆvar( )
xx
x x
x
n s
ì ü-ï ï
a + b = + sí ý
ï ïî þ
An additional 2
s must be added to obtain the variance of the
predicted individual response.
Testing the correlation coefficient
r =
xy
xx yy
s
s s
If 0r = , then 2
2
2
~
1
n
r n
t
r
-
-
-
.
Fisher Z transformation
1
~ ,
3
rz N z
n
r
æ ö
ç ÷è ø-
approximately
where 1 1
2
1
tanh log
1
r
r
z r
r
- +æ ö
= = ç ÷è ø-
and 1 1
2
1
tanh log
1
z -
r
æ ö+ r
= r = ç ÷- rè ø
.
Sum of squares relationship
2 2 2
1 1 1
ˆ ˆ( ) ( ) ( )
n n n
i i i i
i i i
y y y y y y
= = =
- = - + -å å å
32. 26
3.5 ANALYSIS OF VARIANCE
Single factor normal model
2
~ ( , )ij iY N m + t s , 1,2, ,i k= K , 1,2, , ij n= K
where
1
k
i
i
n n
=
= å , with
1
0
k
i i
i
n
=
t =å
Intermediate calculations (sums of squares)
Total:
2
2 2
1 1 1 1
( )
i in nk k
T ij ij
i j i j
y
SS y y y
n= = = =
= - = -åå åå gg
gg
Between treatments:
2 2
2
1 1
( )
k k
i
B i i
ii i
y y
SS n y y
n n= =
= - = -å å g gg
g gg
Residual: R T BSS SS SS= -
Variance estimate
2ˆ RSS
n k
s =
-
Statistical test
Under the appropriate null hypothesis:
1,~
1
B R
k n k
SS SS
F
k n k - -
- -
33. 27
3.6 GENERALISED LINEAR MODELS
Exponential family
For a random variable Y from the exponential family, with natural
parameter q and scale parameter f:
Probability (density) function:
( )
( ; , ) exp ( , )
( )Y
y b
f y c y
a
é ùq - q
q f = + fê úfë û
Mean: ( ) ( )E Y b= q¢
Variance: var( ) ( ) ( )Y a b= f q¢¢
Canonical link functions
Binomial: ( ) log
1
g
m
m =
- m
Poisson: ( ) logg m = m
Normal: ( )g m = m
Gamma:
1
( )g m =
m
34. 28
3.7 BAYESIAN METHODS
Relationship between posterior and prior distributions
Posterior Prior Likelihoodµ ´
The posterior distribution ( | )f xq for the parameter q is related to
the prior distribution ( )f q via the likelihood function ( | )f x q :
( | ) ( ) ( | )f x f f xq µ q ´ q
Normal / normal model
If x is a random sample of size n from a 2
( , )N m s distribution,
where 2
s is known, and the prior distribution for the parameter m is
2
0 0( , )N m s , then the posterior distribution for m is:
2
* *| ~ ( , )x Nm m s
where 0
* 2 2 2 2
0 0
1nx næ ö æ öm
m = + +ç ÷ ç ÷
s s s sè ø è ø
and 2
* 2 2
0
1
1
næ ö
s = +ç ÷
s sè ø
35. 29
3.8 EMPIRICAL BAYES CREDIBILITY – MODEL 1
Data requirements
{ , 1,2, , , 1,2, , }ijX i N j n= =K K
ijX represents the aggregate claims in the j th year from the i th risk.
Intermediate calculations
1
1 n
i ij
j
X X
n =
= å ,
1
1 N
i
i
X X
N =
= å
Parameter estimation
Quantity Estimator
[ ( )]E m q X
2
[ ( )]E s q 2
1 1
1 1
( )
1
N n
ij i
i j
X X
N n= =
ì üï ï
-í ý
-ï ïî þ
å å
var[ ( )]m q 2 2
1 1 1
1 1 1
( ) ( )
1 1
N N n
i ij i
i i j
X X X X
N Nn n= = =
ì üï ï
- - -í ý
- -ï ïî þ
å å å
Credibility factor
2
[ ( )]
var[ ( )]
n
Z
E s
n
m
=
q
+
q
36. 30
3.9 EMPIRICAL BAYES CREDIBILITY – MODEL 2
Data requirements
{ , 1,2, , , 1,2, , }ijY i N j n= =K K , { , 1,2, , , 1,2, , }ijP i N j n= =K K
ijY represents the aggregate claims in the j th year from the i th risk;
ijP is the corresponding risk volume.
Intermediate calculations
1
n
i ij
j
P P
=
= å ,
1
N
i
i
P P
=
= å ,
1
1
* 1
1
N
i
i
i
P
P P
Nn P=
æ ö
= -ç ÷è ø-
å
ij
ij
ij
Y
X
P
= ,
1
n
ij ij
i
ij
P X
X
P=
= å ,
1 1
N n
ij ij
i j
P X
X
P= =
= åå
Parameter estimation
Quantity Estimator
[ ( )]E m q X
2
[ ( )]E s q 2
1 1
1 1
( )
1
N n
ij ij i
i j
P X X
N n= =
ì üï ï
-í ý
-ï ïî þ
å å
var[ ( )]m q 2 2
1 1 1 1
1 1 1 1
( ) ( )
* 1 1
N n N n
ij ij ij ij i
i j i j
P X X P X X
P Nn N n= = = =
- - -
- -
æ ì üö
í ýç ÷è øî þ
åå å å
Credibility factor
1
2
1
[ ( )]
var[ ( )]
n
ij
j
i n
ij
j
P
Z
E s
P
m
=
=
=
q
+
q
å
å
37. 31
4 COMPOUND INTEREST
Increasing/decreasing annuity functions
( )
n
n
n
a nv
Ia
i
-
=
&&
, ( ) n
n
n a
Da
i
-
=
Accumulation factor for variable interest rates
2
1
1 2( , ) exp ( )
t
t
A t t t dt
æ ö
= dç ÷
ç ÷è ø
ò
38. 32
5 SURVIVAL MODELS
5.1 MORTALITY “LAWS”
Survival probabilities
0
exp
t
t x x sp ds+
æ ö
= - mç ÷
è ø
ò
Gompertz’ Law
x
x Bcm = , ( 1)x t
c c
t xp g -
= where logB c
g e-
=
Makeham’s Law
x
x A Bcm = + , ( 1)x t
t c c
t xp s g -
= where A
s e-
=
Gompertz-Makeham formula
The Gompertz-Makeham graduation formula, denoted by GM( , )r s ,
states that
1 2( ) exp[ ( )]x poly t poly tm = +
where t is a linear function of x and 1( )poly t and 2( )poly t are
polynomials of degree r and s respectively.
39. 33
5.2 EMPIRICAL ESTIMATION
Greenwood’s formula for the variance of the Kaplan-Meier
estimator
2
ˆvar[ ( )] 1 ( )
( )
j
j
j j jt t
d
F t F t
n n d£
é ù= -ë û -
å%
Variance of the Nelson-Aalen estimate of the integrated hazard
3
( )
var[ ]
j
j j j
t
t t j
d n d
n£
-
L = å%
5.3 MORTALITY ASSUMPTIONS
Balducci assumption
1 (1 )t x t xq t q- + = - (x is an integer, 0 £ t £ 1)
5.4 GENERAL MARKOV MODEL
Kolmogorov forward differential equation
( )gh gj jh gh hj
t x t x x t t x x t
j h
p p p
t + +
¹
¶
= m - m
¶
å
40. 34
5.5 GRADUATION TESTS
Grouping of signs test
If there are 1n positive signs and 2n negative signs and G denotes the
observed number of positive runs, then:
1 2
1 2
1
1 1
1
( )
n n
t t
P G t
n n
n
- +æ ö æ ö
ç ÷ ç ÷-è ø è ø
= =
+æ ö
ç ÷è ø
and, approximately,
2
1 2 1 2
3
1 2 1 2
( 1) ( )
~ ,
( )
n n n n
G N
n n n n
æ ö+
ç ÷+ +è ø
Critical values for the grouping of signs test are tabulated in the
statistical tables section for small values of 1n and 2n . For larger
values of 1n and 2n the normal approximation can be used.
Serial correlation test
1
2
1
1
( )( )
1
( )
m j
i i j
i
j m
i
i
z z z z
m j
r
z z
m
-
+
=
=
- -
-
»
-
å
å
where
1
1 m
i
i
z z
m =
= å
~ (0,1)jr m N´ approximately.
Variance adjustment factor
2
i
i
x
i
i
i
r
i
p
=
p
å
å
where ip is the proportion of lives at age x who have exactly i
policies.
41. 35
5.6 MULTIPLE DECREMENT TABLES
For a multiple decrement table with three decrements a , b and g ,
each uniform over the year of age ( , 1)x x + in its single decrement
table, then
1 1
2 3
( ) 1 ( )x x x x x xaq q q q q qa a b g b gé ù= - + +
ë û
5.7 POPULATION PROJECTION MODELS
Logistic model
1 ( )
( )
( )
dP t
kP t
P t dt
= r - has general solution ( ) t
P t
C e k-r
r
=
r +
where C is a constant.
42. 36
6 ANNUITIES AND ASSURANCES
6.1 APPROXIMATIONS FOR NON ANNUAL ANNUITIES
( ) 1
2
m
x x
m
a a
m
-
» -&& &&
( )
::
1
1
2
m x n
x nx n
x
Dm
a a
m D
+æ ö-
» - -ç ÷è ø
&& &&
6.2 MOMENTS OF ANNUITIES AND ASSURANCES
Let xK and xT denote the curtate and complete future lifetimes
(respectively) of a life aged exactly x .
Whole life assurances
1
[ ]xK
xE v A+
= , 1 2 2
var[ ] ( )xK
x xv A A+
= -
[ ]xT
xE v A= , 2 2
var[ ] ( )xT
x xv A A= -
Similar relationships hold for endowment assurances (with status
:x n
L ), pure endowments (with status 1
:x n
), term assurances (with
status 1
:x n
) and deferred whole life assurances (with status |m xL ).
Whole life annuities
1
[ ]
x
xK
E a a+
=&& && ,
2 2
1 2
( )
var[ ]
x
x x
K
A A
a
d+
-
=&&
[ ]
x
xT
E a a= ,
2 2
2
( )
var[ ]
x
x x
T
A A
a
-
=
d
Similar relationships hold for temporary annuities (with status :x n
L ).
43. 37
6.3 PREMIUMS AND RESERVES
Premium conversion relationship between annuities and
assurances
1x xA da= - && , 1x xA a= - d
Similar relationships hold for endowment assurance policies (with
status :x n
L ).
Net premium reserve
1 x t
t x
x
a
V
a
+= -
&&
&&
, 1 x t
t x
x
a
V
a
+= -
Similar formulae hold for endowment assurance policies (with
statuses :x n
L and :x t n t+ -
L ).
6.4 THIELE’S DIFFERENTIAL EQUATION
Whole life assurance
(1 )t x t x x t x x tV V P V
t +
¶
= d + - - m
¶
Similar formulae hold for other types of policies.
Multiple state model
( )j j j jk jk k j
t x t x x t x t x t t x t x
k j
V V b b V V
t + + +
¹
¶
= d + - m + -
¶
å
44. 38
7 STOCHASTIC PROCESSES
7.1 MARKOV “JUMP” PROCESSES
Kolmogorov differential equations
Forward equation: ( , ) ( , ) ( )ij ik kj
k S
p s t p s t t
t Î
¶
= s
¶
å
Backward equation: ( , ) ( ) ( , )ij ik kj
k S
p s t s p s t
s Î
¶
= - s
¶
å
where ( )ij ts is the transition rate from state i to state j ( j i¹ ) at
time t , and ii ij
j i¹
s = - så .
Expected time to reach a subsequent state k
,
1 ij
i j
i ij i j k
m m
¹ ¹
s
= +
l l
å , where i ij
j i¹
l = så
7.2 BROWNIAN MOTION AND RELATED PROCESSES
Martingales for standard Brownian motion
If { , 0}tB t ³ is a standard Brownian motion, then the following
processes are martingales:
tB , 2
tB t- and 21
2
exp( )tB tl - l
Distribution of the maximum value
2
0
max ( ) y
s
s t
y t y t
P B s y e
t t
m
£ £
- + m - - mæ ö æ öé ù+ m > = F + Fç ÷ ç ÷ê ú è ø è øë û
, 0y >
45. 39
Hitting times
If
0
min{ : }y s
s
s B s y
³
t = + m = where 0m > and 0y < , then
2
( 2 )
[ ]y y
E e e
-lt m+ m + l
= , 0l >
Ornstein-Uhlenbeck process
t t tdX X dt dB= -g + s , 0g >
7.3 MONTE CARLO METHODS
Box-Muller formulae
If U1 and U2 are independent random variables from the (0,1)U
distribution then
1 1 22log cos(2 )Z U U= - p and 2 1 22log sin(2 )Z U U= - p
are independent standard normal variables.
Polar method
If 1V and 2V are independent random variables from the ( 1,1)U -
distribution and 2 2
1 2S V V= + then, conditional on 0 1S< £ ,
1 1
2logS
Z V
S
-
= and 2 2
2logS
Z V
S
-
=
are independent standard normal variables.
Pseudorandom values from the (0,1)U distribution and the (0,1)N
distribution are included in the statistical tables section.
46. 40
8 TIME SERIES
8.1 TIME SERIES – TIME DOMAIN
Sample autocovariance and autocorrelation function
Autocovariance:
1
1
ˆ ˆ ˆ( )( )
n
k t t k
t k
x x
n
-
= +
g = - m - må , where
1
1
ˆ
n
t
t
x
n =
m = å
Autocorrelation:
0
ˆ
ˆ
ˆ
k
k
g
r =
g
Autocorrelation function for ARMA(1,1)
For the process 1 1t t t tX X e e- -= a + + b :
1
2
(1 )( )
(1 2 )
k
k
-+ ab a + b
r = a
+ b + ab
, 1,2,3,k = K
Partial autocorrelation function
1 1f = r ,
2
2 1
2 2
11
r - r
f =
- r
*
det
det
k
k
k
P
P
f = , 2,3,k = K,
where
1 2 1
1 1 2
2 1 3
1 2 3
1
1
1
1
k
k
kk
k k k
P
-
-
-
- - -
r r ræ ö
ç ÷r r r
ç ÷
r r r= ç ÷
ç ÷
ç ÷
ç ÷r r rè ø
L
L
L
M M M O M
L
and *
kP equals kP , but with the last column replaced with
1 2 3( , , ,..., )T
kr r r r .
47. 41
Partial autocorrelation function for MA(1)
For the process 1t t tX e e -= m + + b :
2
1
2( 1)
(1 )
( 1)
1
k
k
k k
+
+
- b b
f = -
- b
, 1,2,3,k = K
8.2 TIME SERIES – FREQUENCY DOMAIN
Spectral density function
1
( )
2
ik
k
k
f e
¥
- w
=-¥
w = g
p
å , -p < w < p
Inversion formula
( )ik
k e f d
p w
-p
g = w wò
Spectral density function for ARMA(p,q)
The spectral density function of the process ( )( ) ( )t tB X B ef - m = q ,
where 2
var( )te = s , is
2
( ) ( )
( )
2 ( ) ( )
i i
i i
e e
f
e e
w - w
w - w
s q q
w =
p f f
Linear filters
For the linear filter t k t k
k
Y a X
¥
-
=-¥
= å :
2
( ) ( ) ( )Y Xf A fw = w w ,
where ( ) ik
k
k
A e a
¥
- w
=-¥
w = å is the transfer function for the filter.
48. 42
8.3 TIME SERIES – BOX-JENKINS METHODOLOGY
Ljung and Box “portmanteau” test of the residuals for an
ARMA(p,q) model
2
2
( )
1
( 2) ~
m
k
m p q
k
r
n n
n k
- +
=
+ c
-
å
where kr ( 1,2, ,k m= K ) is the estimated value of the k th
autocorrelation coefficient of the residuals and n is the number of
data values used in the ( , )ARMA p q series.
Turning point test
In a sequence of n independent random variables the number of
turning points T is such that:
2
3
( ) ( 2)E T n= - and
16 29
var( )
90
n
T
-
=
49. 43
9 ECONOMIC MODELS
9.1 UTILITY THEORY
Utility functions
Exponential: ( ) aw
U w e-
= - , 0a >
Logarithmic: ( ) logU w w=
Power: 1
( ) ( 1)U w w- g
= g - , 1g £ , 0g ¹
Quadratic: 2
( ) , 0U w w dw d= + <
Measures of risk aversion
Absolute risk aversion:
( )
( )
( )
U w
A w
U w
¢¢
= -
¢
Relative risk aversion: ( ) ( )R w w A w=
9.2 CAPITAL ASSET PRICING MODEL (CAPM)
Security market line
( )i i ME r E r- = b - where
cov( , )
var( )
i M
i
M
R R
R
b =
Capital market line (for efficient portfolios)
( ) P
P M
M
E r E r
s
- = -
s
50. 44
9.3 INTEREST RATE MODELS
Spot rates and forward rates for zero-coupon bonds
Let ( )P t be the price at time 0 of a zero-coupon bond that pays 1 unit
at time t.
Let ( )s t be the spot rate for the period (0, )t .
Let ( )f t be the instantaneous forward rate at time 0 for time t.
Spot rate
( )
( ) s
P e-t t
t = or
1
( ) log ( )s Pt = - t
t
Instantaneous forward rate
0
( ) exp ( )P f s ds
tæ öt = -
è øò or ( ) log ( )
d
f P
d
t = - t
t
Vasicek model
Instantaneous forward rate
( ) (1 ) (1 )f e R e L e e-at -at -at -atb
t = + - + -
a
Price of a zero-coupon bond
2
( ) exp ( ) ( ( )) ( )
2
P D R D L D
bé ù
t = - t - t - t - tê úë û
where
1
( )
e
D
-at
-
t =
a
51. 45
10 FINANCIAL DERIVATIVES
Note. In this section, q denotes the (continuously-payable) dividend
rate.
10.1 PRICE OF A FORWARD OR FUTURES CONTRACT
For an asset with fixed income of present value I:
0( ) rT
F S I e= -
For an asset with dividends:
( )
0
r q T
F S e -
=
10.2 BINOMIAL PRICING (“TREE”) MODEL
Risk-neutral probabilities
Up-step probability
r t
e d
u d
D
-
=
-
,
where t q t
u es D + D
»
and t q t
d e-s D + D
» .
52. 46
10.3 STOCHASTIC DIFFERENTIAL EQUATIONS
Generalised Wiener process
dx adt bdz= +
where a and b are constant and dz is the increment for a Wiener
process (standard Brownian motion).
Ito process
( , ) ( , )dx a x t dt b x t dz= +
Ito’s lemma for a function G(x, t)
2
2
2
1
2
G G G G
dG a b dt b dz
x t xx
æ ö¶ ¶ ¶ ¶
= + + +ç ÷¶ ¶ ¶¶è ø
Models for the short rate tr
Ho-Lee: ( )dr t dt dz= q + s
Hull-White: [ ( ) ]dr t ar dt dz= q - + s
Vasicek: ( )dr a b r dt dz= - + s
Cox-Ingersoll-Ross: ( )dr a b r dt rdz= - + s
10.4 BLACK-SCHOLES FORMULAE FOR EUROPEAN OPTIONS
Geometric Brownian motion model for a stock price tS
( )t tdS S dt dz= m + s
Black-Scholes partial differential equation
2
2 21
2 2
( ) t t
t t
f f f
r q S S rf
t S S
¶ ¶ ¶
+ - + s =
¶ ¶ ¶
53. 47
Garman-Kohlhagen formulae for the price of call and put options
Call: ( ) ( )
1 2( ) ( )q T t r T t
t tc S e d Ke d- - - -
= F - F
Put: ( ) ( )
2 1( ) ( )r T t q T t
t tp Ke d S e d- - - -
= F - - F -
where
2
1
log( ) ( ½ )( )tS K r q T t
d
T t
+ - + s -
=
s -
and
2
2 1
log( ) ( ½ )( )tS K r q T t
d d T t
T t
+ - - s -
= = - s -
s -
10.5 PUT-CALL PARITY RELATIONSHIP
( ) ( )r T t q T t
t t tc Ke p S e- - - -
+ = +
73. 67
POPULATION MORTALITY TABLE
ELT15 (Males) and ELT15 (Females)
This table is based on the mortality of the population of England and Wales
during the years 1990, 1991, and 1992. Full details are given in English Life
Tables No. 15 published by The Stationery Office.
Note that no 0m values have been included because of the difficulty of
calculating reasonable estimates from observed data.