. 15
( 19 .)


From our formulas for stochastic integrals, this means
|Hs ’ Hs |2 ds ’ 0.
n m

This says that Hs is a Cauchy sequence in the space L2 (with respect to the norm ·
by Y 2 = E 0 Ys2 ds ). Measure theory tells us that L2 is a complete metric space, so
there exists Hs such that
|Hs ’ Hs |2 ds ’ 0.
In particular Hs ’ Hs , and this implies Hs is adapted. Another consequence, due to Fatou™s
t 2
lemma, is that E 0 Hs ds.
Let Ut = 0 Hs dWs . Then as above,
n 2
(Hs ’ Hs )2 ds ’ 0.
E |(V ’ cn ) ’ Ut | = E

Therefore Ut = V ’ c, and U has the desired form.

Note 3. Here is the proof of Proposition 18.3. By Ito™s formula with Xs = ’iuWs + u2 s/2
and f (x) = ex ,
t t
Xt Xs
eXs (u2 /2)ds
e =1+ e (’iu)dWs +
0 0
eXs (’iu)2 ds
+ 2
eXs dWs .
= 1 ’ iu
If we multiply both sides by e’u t/2
, which is a constant and hence adapted, we obtain
’iuWt u
e = cu + Hs dWs (18.3)

for an appropriate constant cu and integrand H u .
If f is a smooth function (e.g., C ∞ with compact support), then its Fourier transform
f will also be very nice. So if we multiply (18.3) by f (u) and integrate over u from ’∞ to
∞, we obtain
f (Wt ) = c + Hs dWs
for some constant c and some adapted integrand H. (We implicitly used Proposition 18.2,
because we approximate our integral by Riemann sums, and then take a limit.) Now using
Proposition 18.2 we take limits and obtain the proposition.

Note 4. The argument is by induction; let us do the case n = 2 for clarity. So we suppose

V = f (Wt )g(Wu ’ Wt ).

From Proposition 18.3 we now have that
t u
g(Wu ’ Wt ) = d +
f (Wt ) = c + Hs dWs , Ks dWs .
0 t

Set H r = Hr if 0 ¤ s < t and 0 otherwise. Set K r = Kr if s ¤ r < t and 0 otherwise. Let
s s
Xs = c + 0 H r dWr and Ys = d + 0 K r dWr . Then
X, Y = H r K r dr = 0.

Then by the Ito product formula,
s s
Xs Ys = X0 Y0 + Xr dYr + Yr dXr
0 0
+ X, Y s
= cd + [Xr K r + Yr H r ]dWr .

If we now take s = u, that is exactly what we wanted. Note that Xr K r + Yr H r is 0 if r > u;
this is needed to do the general induction step.

19. Completeness.
Now let Pt be a geometric Brownian motion. As we mentioned in Section 16, if
Pt = P0 exp(σWt + (µ ’ r ’ σ 2 /2)t), then given Pt we can determine Wt and vice versa,
so the σ ¬elds generated by Pt and Wt are the same. Recall Pt satis¬es

dPt = σPt dWt + (µ ’ r)Pt dt.

De¬ne a new probability P by
= Mt = exp(aWt ’ a2 t/2).
By the Girsanov theorem,
Wt = Wt ’ at
is a Brownian motion under P. So

dPt = σPt dWt + σPt adt + (µ ’ r)Pt dt.

If we choose a = ’(µ ’ r)/σ, we then have

dPt = σPt dWt . (19.1)

Since Wt is a Brownian motion under P, then Pt must be a martingale, since it is a
stochastic integral of a Brownian motion. We can rewrite (19.1) as

dWt = σ ’1 Pt’1 dPt . (19.2)

Given a Ft measurable variable V , we know by Theorem 18.1 that there exist a
t 2
constant and an adapted process Hs such that E 0 Hs ds < ∞ and
V =c+ Hs d W s .

But then using (19.2) we have
Hs σ ’1 Ps dPs .
V =c+

We have therefore proved
Theorem 19.1. If Pt is a geometric Brownian motion and V is Ft measurable and square
integrable, then there exist a constant c and an adapted process Ks such that
V =c+ Ks dPs .

Moreover, there is a probability P under which Pt is a martingale.
The probability P is called the risk-neutral measure. Under P the present day value
of the stock price is a martingale.

20. Black-Scholes formula, I.
We can now derive the formula for the price of any option. Let T ≥ 0 be a ¬xed
real. If V is FT measurable, we have by Theorem 19.1 that

V =c+ Ks dPs , (20.1)

and under P, the process Ps is a martingale.

Theorem 20.1. The price of V must be E V .

Proof. This is the “no arbitrage” principle again. Suppose the price of the option V at
time 0 is W . Starting with 0 dollars, we can sell the option V for W dollars, and use the
W dollars to buy and trade shares of the stock. In fact, if we use c of those dollars, and
invest according to the strategy of holding Ks shares at time s, then at time T we will
erT (W0 ’ c) + V

dollars. At time T the buyer of our option exercises it and we use V dollars to meet that
obligation. That leaves us a pro¬t of erT (W0 ’ c) if W0 > c, without any risk. Therefore
W0 must be less than or equal to c. If W0 < c, we just reverse things: we buy the option
instead of sell it, and hold ’Ks shares of stock at time s. By the same argument, since
we can™t get a riskless pro¬t, we must have W0 ≥ c, or W0 = c.
Finally, under P the process Pt is a martingale. So taking expectations in (20.1),
we obtain
E V = c.

The formula in the statement of Theorem 20.1. is amenable to calculation. Suppose
we have the standard European option, where

V = e’rt (St ’ K)+ = (e’rt St ’ e’rt K)+ = (Pt ’ e’rt K)+ .

Recall that under P the stock price satis¬es

dPt = σPt dWt ,

where Wt is a Brownian motion under P. So then
Pt = P0 eσWt ’σ t/2


E V = E [(PT ’ e’rT K)+ ] (20.2)
= E [(P0 eσWT ’(σ ’ e’rT K)+ ].

We know the density of WT is just (2πT )’1/2 e’y /(2T ) , so we can do some calculations
(see Note 1) and end up with the famous Black-Scholes formula:

W0 = x¦(g(x, T )) ’ Ke’rT ¦(h(x, T )),

z 2
e’y /2
where ¦(z) = dy, x = P0 = S0 ,

log(x/K) + (r + σ 2 /2)T

g(x, T ) = ,

h(x, T ) = g(x, T ) ’ σ T .

It is of considerable interest that the ¬nal formula depends on σ but is completely
independent of µ. The reason for that can be explained as follows. Under P the process Pt
satis¬es dPt = σPt dWt , where Wt is a Brownian motion. Therefore, similarly to formulas
we have already done,
Pt = P0 eσWt ’σ t/2 ,

and there is no µ present here. (We used the Girsanov formula to get rid of the µ.) The
price of the option V is
E [PT ’ e’rT K]+ , (20.3)

which is independent of µ since Pt is.

Note 1. We want to calculate
E (xeσWT ’σ ’ e’rT K)+ ,
T /2

where Wt is a Brownian motion under P and we write x for P0 = S0 . Since WT is a normal

random vairable with mean 0 and variance T , we can write it as T Z, where Z is a standard
mean 0 variance 1 normal random variable.
Now √
σ T Z’σ 2 T /2
> e’rT K

if and only if

log x + σ T Z ’ σ 2 T /2 > ’r + log K,

or if
Z > (σ 2 T /2) ’ r + log K ’ log x.


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