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I need the martingale part of the put payoff (not $C^2$..). Where $S_t=exp(\sigma W_t -\frac{\sigma^2t}{2})$

$d[(S_t -K)^+ ]$ ??

I guess I need to use local times but how?

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up vote 2 down vote accepted

Thanks you all!

(proof, for $\phi(t,S_t)=(K-S_t)^+$:

Step 1 smoothing : $\phi_\epsilon(x)=1_{S_t\leq K+\epsilon}\cdot\phi(x)+1_{S_t\in]K-\epsilon,K]} \cdot \psi(x)$, where $\psi(x)=-\frac{1}{\epsilon^2}(K-x)^2(K-x-2\epsilon)$ .

This function is $C^1$, and also $C^2$ excepting in a countable set.

Step 2 Itô on $\phi_\epsilon(S_t)= \phi_\epsilon(S_0)+\int^t_0\phi_\epsilon^{'}(S_t)dS_t+\frac{1}{2}\int^t_0 1_{S_t\in[K-\epsilon,K]}\phi_\epsilon^{''}(S_t)d\langle S\rangle _t$ because $\phi _\epsilon=0$ out of $[K-\epsilon,K]$

Let's denote by $L_t=lim_{\epsilon \to0}{\frac{1}{2\epsilon^2}*\int{_{K-\epsilon}^K(3S_t+4\epsilon-3K)dS_t}}$ it's a finite variation process since it is increasing

Step 3 We have that $\phi_\epsilon(S_t)-\phi_\epsilon(S_0)-\int^t_0\phi_\epsilon^{'}(S_t)dS_t \space \xrightarrow {L^2} \space\phi(S_t) -\phi(S_0) -\int^t_0\phi^{'}(S_t)dS_t $ (because $\int^t_0\phi_\epsilon^{'}(S_t) 1_{S_u\in[0,K-\epsilon]}dS_u \xrightarrow {L^2}\int_0^t\phi^{'}(S_u)du$ by using Itô isometry)

Finally We get the formula 'à la bridge' namely

$(K-S_t)^+=(K-S_0)^+-\int_0^t1_{K\leq S_u}dS_u+L_t$

and the martingale part is

$(K-S_0)^+-\int_0^t1_{K\leq S_u}\sigma S_u dB_u$

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I think that the half factor difference on the Local Ltime term that we have is about the way Local Time is deifned. Different "autors" use different conventions (multiplying $L_t$ by a factor 2 or not in their definition). The definition I use in my answer doesn't use the factor "2" and yours use it. Regards. – The Bridge Mar 7 '11 at 17:07


Simply use Itô-Tanaka's formula I guess this should give something like : $df(S_t)=D_-f(S_t)dS_t+\frac{1}{2}dL^s_tf''(ds)$

with $f(S)=(S-K)^+$ so $D_-f(S)=1_{]K,+\infty}(S)$ and $f''(ds)=\delta_K(ds)$

This gives if I am not mistaken :


With $L^K_t$ being the local time of your geometric Brownian Motion $S$ around level $K$ at time $t$.


Edit NB:
-$D_-$ stands for the left derivative of $f$

-$f''(ds)$ stands for second derivatives in the distribution-sense.

-The use of Itô-Tanaka's formula allows to avoid the derivation of the Mollifiers-type argument for the direct proof of the result (which is quite cumbersome in my opinion). I should add that Ito-Tanaka's formula is applicable to every $f$ that is the differnce of two convex functions if I remember well, which is the case here with $f(x)=(X-K)^+$.

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So, the martingale part is $\sigma\int1_{[K,\infty)}(S)S\,dW$, which was the question. – George Lowther Mar 6 '11 at 17:02
@george lowther: that's right thanks for the precision – The Bridge Mar 6 '11 at 18:50

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