Chapter 4
Stochastic Processes
Financial variables, such as prices and returns, are random time-
dependent variables. The notion of stochastic process is used to de-
scribe their behavior. Specifically, the Wiener process (or the Brownian
motion) plays the central role in mathematical finance. Section 4.1
begins with the generic path: Markov process ! Chapmen-Kolmo-
gorov equation ! Fokker-Planck equation ! Wiener process. This
methodology is supplemented with two other approaches in Section
4.2. Namely, the Brownian motion is derived using the Langevin’s
equation and the discrete random walk. Then the basics of stochastic
calculus are described. In particular, the stochastic differential equa-
tion is defined using the Ito’s lemma (Section 4.3), and the stochastic
integral is given in both the Ito and the Stratonovich forms
(Section 4.4). Finally, the notion of martingale, which is widely popu-
lar in mathematical finance, is introduced in Section 4.5.
4.1 MARKOV PROCESSES
Consider a process X(t) for which the values x
1
,x
2
, ... are meas-
ured at times t
1
,t
2
, ... Here, one-dimensional variable x is used
for notational simplicity, though extension to multidimensional
systems is trivial. It is assumed that the joint probability density
f(x
1
,t
1
;x
2
,t
2
; ...) exists and defines the system completely. The con-
ditional probability density function is defined as
29
f(x
1
,t
1
;x
2
,t
2
; ...x
k
,t
k
jx
kþ1
,t
kþ1
;x
kþ2
,t
kþ2
; ...) ¼
f(x
1
,t
1
;x
2
,t
2
; ...x
kþ1
,t
kþ1
; ...)=f(x
kþ1
,t
kþ1
;x
kþ2
,t
kþ2
; ...)(4:1:1)
In (4.1.1) and further in this section, t
1
> t
2
> ...t
k
> t
kþ1
> ...
unless stated otherwise. In the simplest stochastic process, the present
has no dependence on the past. The probability density function for
such a process equals
f(x
1
,t
1
;x
2
,t
2
; ...) ¼ f(x
1
,t
1
)f(x
2
,t
2
) ...
Y
i
f(x
i
,t
i
)(4:1:2)
The Markov process represents the next level of complexity, which
embraces an extremely wide class of phenomena. In this process, the
future depends on the present but not on the past. Hence, its condi-
tional probability density function equals
f(x
1
,t
1
;x
2
,t
2
; ...x
k
,t
k
jx
kþ1
,t
kþ1
;x
kþ2
,t
kþ2
; ...) ¼
f(x
1
,t
1
;x
2
,t
2
; ...x
k
,t
k
jx
kþ1
,t
kþ1
)(4:1:3)
This means that evolution of the system is determined with the initial
condition (i.e., with the value x
kþ1
at time t
kþ1
). It follows for the
Markov process that
f(x
1
,t
1
;x
2
,t
2
;x
3
,t
3
) ¼ f(x
1
,t
1
jx
2
,t
2
)f(x
2
,t
2
jx
3
,t
3
)(4:1:4)
Using the definition of the conditional probability density, one can
introduce the general equation
f(x
1
,t
1
jx
3
,t
3
) ¼
ð
f(x
1
,t
1
;x
2
,t
2
jx
3
,t
3
)dx
2
¼
ð
f(x
1
,t
1
jx
2
,t
2
;x
3
,t
3
)f(x
2
,t
2
jx
3
,t
3
)dx
2
(4:1:5)
For the Markov process,
f(x
1
,t
1
jx
2
,t
2
;x
3
,t
3
) ¼ f(x
1
,t
1
jx
2
,t
2
), (4:1:6)
Then the substitution of equation (4.1.6) into equation (4.1.5) leads to
the Chapmen-Kolmogorov equation
f(x
1
,t
1
jx
3
,t
3
) ¼
ð
f(x
1
,t
1
jx
2
,t
2
)f(x
2
,t
2
jx
3
,t
3
)dx
2
(4:1:7)
This equation can be used as the starting point for deriving the
Fokker-Planck equation (see, e.g., [1] for details). First, equation
(4.1.7) is transformed into the differential equation
30 Stochastic Processes
@
@t
f(x, tjx
0
,t
0
) ¼
@
@x
[A(x, t)f(x, tjx
0
,t
0
)] þ
1
2
@
2
@x
2
[D(x, t)f(x, tjx
0
,t
0
)]þ
ð
[R(xjz, t)f(z, tjx
0
,t
0
) R(zjx, t)f(x, tjx
0
,t
0
)]dz (4:1:8)
In (4.1.8), the drift coefficient A(x, t) and the diffusion coefficient
D(x, t) are equal
A(x, t) ¼ lim
Dt!0
1
Dt
ð
(z x)f(z, t þ Dtjx, t)dz (4:1:9)
D(x, t) ¼ lim
Dt!0
1
Dt
ð
(z x)
2
f(z, t þ Dtjx, t)dz (4:1:10)
The integral in the right-hand side of the Chapmen-Kolmogorov
equation (4.1.8) is determined with the function
R(xjz, t) ¼ lim
Dt!0
1
Dt
f(x, t þ Dtjz, t) (4:1:11)
It describes possible discontinuous jumps of the random variable. Neg-
lecting this term in equation (4.1.8) yields the Fokker-Planck equation
@
@t
f(x, tjx
0
,t
0
) ¼
@
@x
[A(x, t)f(x, tjx
0
,t
0
)]
þ
1
2
@
2
@x
2
[D(x, t)f(x, tjx
0
,t
0
)]
(4:1:12)
This equation with A(x, t) ¼ 0 and D ¼ const is reduced to the
diffusion equation that describes the Brownian motion
@
@t
f(x, tjx
0
,t
0
) ¼
D
2
@
2
@x
2
f(x, tjx
0
,t
0
)(4:1:13)
Equation (4.1.13) has the analytic solution in the Gaussian form
f(x, tjx
0
,t
0
) ¼ [2pD(t t
0
)]
1=2
exp [(x x
0
)
2
=2D(t t
0
)] (4:1:14)
Mean and variance for the distribution (4.1.14) equal
E[x(t)] ¼ x
0
, Var[x(t)] ¼ E[(x(t) x
0
)
2
] ¼ s
2
¼ D(t t
0
)(4:1:15)
The diffusion equation (4.1.13) with D ¼ 1 describes the standard
Wiener process for which
E[(x(t) x
0
)
2
] ¼ t t
0
(4:1:16)
Stochastic Processes 31

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