Probability for Finance
Patrick Roger
Download free books at
Probability for Finance
Patrick Roger
Strasbourg University, EM Strasbourg Business School
May 2010
Download free eBooks at bookboon.com
2
Probability for Finance
© 2010 Patrick Roger & Ventus Publishing ApS
ISBN 978-87-7681-589-9
Download free eBooks at bookboon.com
3
Contents
Probability for Finance
Contents
Introduction
8
1.
1.1
1.1.1
1.1.2
1.1.3
1.2
1.2.1
1.2.2
1.2.3
1.3
1.3.1
1.3.2
1.3.3
1.3.4
1.3.5
Probability spaces and random variables
Measurable spaces and probability measures
σ algebra (or tribe) on a set Ω
Sub-tribes of A
Probability measures
Conditional probability and Bayes theorem
Independant events and independant tribes
Conditional probability measures
Bayes theorem
Random variables and probability distributions
Random variables and generated tribes
Independant random variables
Probability distributions and cumulative distributions
Discrete and continuous random variables
Transformations of random variables
10
10
11
13
16
18
19
21
24
25
25
29
30
34
35
2.
2.1
Moments of a random variable
Mathematical expectation
37
37
Fast-track
your career
Masters in Management
Stand out from the crowd
Designed for graduates with less than one year of full-time postgraduate work
experience, London Business School’s Masters in Management will expand your
thinking and provide you with the foundations for a successful career in business.
The programme is developed in consultation with recruiters to provide you with
the key skills that top employers demand. Through 11 months of full-time study,
you will gain the business knowledge and capabilities to increase your career
choices and stand out from the crowd.
London Business School
Regent’s Park
London NW1 4SA
United Kingdom
Tel +44 (0)20 7000 7573
Email
Applications are now open for entry in September 2011.
For more information visit www.london.edu/mim/
email or call +44 (0)20 7000 7573
www.london.edu/mim/
Download free eBooks at bookboon.com
4
Click on the ad to read more
Contents
Probability for Finance
2.1.1
2.1.2
2.1.3
2.2
2.2.1
2.2.2
2.3
2.3.1
2.3.2
2.3.3
2.3.4
2.4
2.4.1
2.4.2
2.5
2.5.1
2.5.2
Expectations of discrete and continous random variables
Expectation: the general case
Illustration: Jensen’s inequality and Saint-Peterburg paradox
Variance and higher moments
Second-order moments
Skewness and kurtosis
The vector space of random variables
Almost surely equal random variables
The space L1 (Ω, A, P)
The space L2 (Ω, A, P)
Covariance and correlation
Equivalent probabilities and Radon-Nikodym derivatives
Intuition
Radon Nikodym derivatives
Random vectors
Definitions
Application to portfolio choice
39
40
43
46
46
48
50
51
53
54
59
63
63
67
69
69
71
3.
3.1
3.1.1
3.1.2
3.1.3
Usual probability distributions in financial models
Discrete distributions
Bernoulli distribution
Binomial distribution
Poisson distribution
73
73
73
76
78
Download free eBooks at bookboon.com
5
Click on the ad to read more
Contents
Probability for Finance
3.2
3.2.1
3.2.2
3.2.3
3.3
3.3.1
3.3.2
3.3.3
Continuous distributions
Uniform distribution
Gaussian (normal) distribution
Log-normal distribution
Some other useful distributions
2
The X distribution
The Student-t distribution
The Fisher-Snedecor distribution
81
81
82
86
91
91
92
93
4.
4.1
4.1.1
4.1.2
4.1.3
4.1.4
4.1.5
4.2
4.2.1
4.2.2
4.3
4.3.1
4.4
4.4.1
Conditional expectations and Limit theorems
Conditional expectations
Introductive example
Conditional distributions
Conditional expectation with respect to an event
Conditional expectation with respect to a random variable
Conditional expectation with respect to a substribe
Geometric interpretation in L2 (Ω, A, P)
Introductive example
Conditional expectation as a projection in L2
Properties of conditional expectations
The Gaussian vector case
The law of large numbers and the central limit theorem
Stochastic Covergences
94
94
94
96
97
98
100
101
101
102
104
105
108
108
your chance
to change
the world
Here at Ericsson we have a deep rooted belief that
the innovations we make on a daily basis can have a
profound effect on making the world a better place
for people, business and society. Join us.
In Germany we are especially looking for graduates
as Integration Engineers for
• Radio Access and IP Networks
• IMS and IPTV
We are looking forward to getting your application!
To apply and for all current job openings please visit
our web page: www.ericsson.com/careers
Download free eBooks at bookboon.com
6
Click on the ad to read more
Contents
Probability for Finance
4.4.2
4.4.3
Law of large numbers
Central limit theorem
109
112
Bibliography
114
I joined MITAS because
I wanted real responsibili�
I joined MITAS because
I wanted real responsibili�
Real work
International
Internationa
al opportunities
�ree wo
work
or placements
�e Graduate Programme
for Engineers and Geoscientists
Maersk.com/Mitas
www.discovermitas.com
M
Month 16
I was a construction
M
supervisor
ina cons
I was
the North Sea super
advising and the No
he
helping
foremen advis
ssolve
problems
Real work
he
helping
f
International
Internationa
al opportunities
�ree wo
work
or placements
ssolve p
Download free eBooks at bookboon.com
7
�e
for Engin
Click on the ad to read more
Introduction
Probability for Finance
Download free eBooks at bookboon.com
8
Probability for Finance
Introduction
DTU Summer University
– for dedicated international students
Spend 3-4 weeks this summer at the highest ranked
Application deadlines
and
programmes:
technical
university in Scandinavia.
31
15
30
3
DTU’s English-taught Summer University is for dedicated
international BSc students of engineering or related
natural science programmes.
March Arctic Technology
March & 15 April Chemical/Biochemical Engineering
April Telecommunication
June Food Entrepreneurship
Visit us at www.dtu.dk
Download free eBooks at bookboon.com
9
Click on the ad to read more
Probability spaces and random variables
Probability for Finance
t = 0 T = 1.
.
T
P
P
Download free eBooks at bookboon.com
10
Probability spaces and random variables
Probability for Finance
,
σ
σ
P()
σ A P()
∈ A
∀ B ∈ A, B c ∈ A B c B B c =
{ω ∈ /ω ∈
/ B} . A
(Bn , n ∈ N) A, +∞
n=1 Bn ∈ A.
A
(, A) A
T = 1 ω
A ω ∈ A A ω ∈
/ A.
, .
= {ω1 , ω 2 , ω 3 , ω 4 } ,
A = {∅, } A′ =
{∅, {ω 1 , ω 2 } , {ω 3 , ω 4 } , } A = P(),
A
(Bn , n ∈ N) A, ∩+∞
n=1 Bn ∈
A A
∅ ∈ A.
σ
σ
Download free eBooks at bookboon.com
11
Probability spaces and random variables
Probability for Finance
Γ = {B1 , ..., BK }
Bi ∩ Bj = ∅ i = j
∪K
i=1 Bi = .
A
A.
A
Γ,
∅, Γ .
Bj
Bj ) Γ
Bj Γ Bj
∅
Brain power
By 2020, wind could provide one-tenth of our planet’s
electricity needs. Already today, SKF’s innovative knowhow is crucial to running a large proportion of the
world’s wind turbines.
Up to 25 % of the generating costs relate to maintenance. These can be reduced dramatically thanks to our
systems for on-line condition monitoring and automatic
lubrication. We help make it more economical to create
cleaner, cheaper energy out of thin air.
By sharing our experience, expertise, and creativity,
industries can boost performance beyond expectations.
Therefore we need the best employees who can
meet this challenge!
The Power of Knowledge Engineering
Plug into The Power of Knowledge Engineering.
Visit us at www.skf.com/knowledge
Download free eBooks at bookboon.com
12
Click on the ad to read more
Probability spaces and random variables
Probability for Finance
Γ = {B1 , ..., BK }
Γ, BΓ ,
Γ.
BΓ
BΓ ∅, ,
Γ.
BΓ 2K
A
T > 1
T
P) t < T,
P).
A′ P) A A′
A A
A′ .
, A′ ) A′
= {ω 1 , ω 2 , ω 3 , ω 4 } ,
A′ = {∅, {ω 1 , ω 2 } , {ω 3 , ω 4 } , } P).
∈ A′ B A′ B c A′ {ω 1 , ω 2 } =
{ω 3 , ω 4 }c . A′ A′
{ω 1 , ω 2 } ∪ {ω 3 , ω 4 } = .
A
A′ A′ ⊂ A Γ Γ′
Card( Card(
Card( < Card(P(.
P(
Download free eBooks at bookboon.com
13
Probability spaces and random variables
Probability for Finance
Γ Γ′
Γ′ Γ. Γ
Γ′ .
A′ A Γ A
Γ′ A′ .
2K
K K
A′ A;
u
d),
= {uu; ud; du; dd}
A′ = {∅; {uu; ud} ; {du; dd} ; }
P). {du; dd}
= {uu; ud}c
{uu; ud} {du; dd} = ∈ A.
1
ր
ց
ր
u
ց
d
ր
ց
uu = u2
ud
du
dd = d2
{uu; ud}
.
{uu; ud} .
ud du
ud
Γ′ Γ.
Download free eBooks at bookboon.com
14
Probability spaces and random variables
Probability for Finance
du.
R,
BR .
R R. BR
The financial industry needs a strong software platform
That’s why we need you
SimCorp is a leading provider of software solutions for the financial industry. We work together to reach a common goal: to help our clients
succeed by providing a strong, scalable IT platform that enables growth, while mitigating risk and reducing cost. At SimCorp, we value
commitment and enable you to make the most of your ambitions and potential.
Are you among the best qualified in finance, economics, IT or mathematics?
Find your next challenge at
www.simcorp.com/careers
www.simcorp.com
MITIGATE RISK
REDUCE COST
ENABLE GROWTH
Download free eBooks at bookboon.com
15
Click on the ad to read more
Probability spaces and random variables
Probability for Finance
(, A)
A A [0; 1]
P () = 1
(Bn , n ∈ N) A
+∞ +∞
P
Bn =
P (Bn )
n=1
n=1
(, A, P )
∅
B B c ,
P (B) + P (B c ) = P () = 1
P (B c ) = 1−P (B).
B B c
σ
Download free eBooks at bookboon.com
16
Probability spaces and random variables
Probability for Finance
(, A, P )
P (∅) = 0
∀ (B1 , B2 ) ∈ A × A, B1 ⊆ B2 ⇒ P (B1 ) ≤ P (B2 )
(Bn , n ∈ N) Bn ⊂ Bn+1
A
lim P (Bn ) = P
Bn
n→+∞
n∈N
(Bn , n ∈ N) Bn ⊃ Bn+1
A
Bn
lim P (Bn ) = P
n→+∞
n∈N
∀ B ∈ A, P (B c ) = 1 − P (B)
∅ P ( ∅) = P () + P (∅) =
P () = 1. P (∅) = 0
B1 ⊆ B2 ⇒ P (B2 ) = P (B1 (B2 B1c )) = P (B1 ) + P (B2 B1c ) ≥
P (B1 )
n
(Bn , n ∈ N) un = P
p=1 Bp
P () = 1
(Bn , n ∈N)
P
n∈N Bn .
n
(Bn , n ∈ N) vn = P
B
p
p=1
P (∅) = 0
(Bn , n ∈N)
P
n∈N Bn .
P (B B c ) = P (B)
+ P (B c ) B
B c B B c = , P (B B c ) = P () = 1
P (B c ) = 1 − P (B)
Download free eBooks at bookboon.com
17
Probability spaces and random variables
Probability for Finance
Card() = N A = P() ;
A
1
∀ω ∈ , P (ω) =
N
[0; 1] × [0; 1]
R2 ; σ
A
, P (A) A P P () = 1;
P
[0; 1] × [0; 1]
B = [a; b] × [c; d] (d − c)(b − a) ≤ 1.
B (d − c)(b − a).
(, A, P )
B ⊂
A
P (ω)
P ({ω})
Download free eBooks at bookboon.com
18
Probability spaces and random variables
Probability for Finance
B1 , B2 A P (B1
P (B1 ) × P (B2 ).
B2 ) =
B2 ∈ A P (B2 ) = 0 B1
B2 P (B1 |B2 ),
P (B1 B2 )
P (B1 |B2 ) =
P (B2 )
B2
B2 . B1
B2 ,
. B1 B2 = ∅, B1
B1
B1 B2
B2 B1 .
B1 B2
P (B1 B2 )
P (B1 ) × P (B2 )
P (B1 |B2 ) =
=
= P (B1 )
P (B2 )
P (B2 )
= [0; 1] × [0; 1]
(x, y) B1 = 0; 12 ×
1
; 1 B2 = 0; 13 × 0; 12 ;
3
1 2
1
× =
2 3
3
1 1
1
P (B2 ) =
× =
3 2
6
P (B1 ) =
Download free eBooks at bookboon.com
19
Probability spaces and random variables
Probability for Finance
B2 (x, y) ∈ B1 x ∈ 0; 13 y
1/3 13 ; 12 . (x, y) ∈ B2
y ≤ 12 . (x, y)
B1 y ≥ 13,
1/3
y ∈ 13 ; 12 .
y ∈ 0; 12
P (B1 |B2 ) = 13 B1 B2 = 0; 13 × 13 ; 12 ,
1
1 1
1
P (B1 B2 ) =
−0 ×
−
=
3
2 3
18
P (B1 |B2 ) =
1
18
1
6
=
1
= P (B1 )
3
B1 B2 .
Download free eBooks at bookboon.com
20
Click on the ad to read more
Probability spaces and random variables
Probability for Finance
B1 B2
B1 ,
B2 B1
σ
G G ′ A
∀B ∈ G, ∀B ′ ∈ G ′ , P (B ∩ B ′ ) = P (B) × P (B ′ )
G× G ′
P()
(, A).
∅.
B ∈ A AB
AB = A B A ∈ A ;
AB B. (B, AB )
Download free eBooks at bookboon.com
21
Probability spaces and random variables
Probability for Finance
B ∈ AB
B = B (Cn , n ∈ N)
Cn = An B
Cn =
An
An B =
B
n∈N
n∈N
n∈N
B ∈ AB
An ∈ A
n∈N An
C = A B ∈ AB CBc C B.
c
B = Ac B
Bc B
CBc = A B
= Ac B ∈ AB
A
n∈N
B ∈ P (B) = 0
P (. |B ), P (B1 |B ) B1 ,
(B, AB ) .
P (B |B ) = 1. (Cn , n ∈ N)
AB
P
P
B
B)
n∈N Cn
n∈N (Cn
P
Cn |B =
=
P (B)
P (B)
n∈N
n, Cn ⊂ B,
P
C
B)
n
n∈N
n∈N P (Cn )
n∈N P (Cn
=
=
=
P (Cn |B )
P (B)
P (B)
P (B)
n∈N
t B.
Download free eBooks at bookboon.com
22
Probability spaces and random variables
Probability for Finance
(B, AB , P (. |B )).
Download free eBooks at bookboon.com
23
Click on the ad to read more
Probability spaces and random variables
Probability for Finance
(B1 , B2 , ..., Bn ) C ∈ A,
P (C |Bj )P (Bj )
P (Bj |C ) = n
i=1 P (C |Bi )P (Bi )
Bj
n
C=
C
Bi
i=1
P (C) =
n
i=1
n
P C
Bi =
P (C |Bi )P (Bi )
i=1
P C
Bj = P (C |Bj )P (Bj ) = P (Bj |C )P (C)
Download free eBooks at bookboon.com
24
Probability spaces and random variables
Probability for Finance
P (C)
C
B1 B2 = B1c
P (B1 |C ) =
P (C |B1 )P (B1 )
P (C |B1 )P (B1 ) + P (C |B2 )P (B2 )
P (B1 ) = 10−4
P (C |B1 ) = 0.99
P (C |B2 ) = 0.01
P (B1 |C ) =
0.99 × 10−4
≃ 0.01
0.99 × 10−4 + 0.01 × (1 − 10−4 )
T = 1
R+
R
(S − S )/S ,
ln(S /S ), −∞.
Download free eBooks at bookboon.com
25