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## Hot answers tagged itos-lemma

14

A common way to use Ito's lemma is also to solve the SDEs. The most classic example (I guess) is the geometric Brownian motion: $$dX_t = \mu X_t dt + \sigma X_t dW_t$$ and this can be solved easily by applying Itô's lemma with $$f(x)=\ln(x)$$ That's the BnB example: $$f'(x)=\frac{1}{x}$$ $$f''(x)=-\frac{1}{x^2}$$ and by Itô: ...

11

The difference between the two is that the first will lead you to a discretization scheme of the process. So you will have to simulate a whole (approximate) trajectory of (meaning by that $X'_{t_0},...,X'_{t_n}$) up to time $T$ (the expiry of your vanilla option) to get to $X'_T$ which is then only an approximation of $X_T$. The second method is exact and ...

11

Baxter and Rennie say it better than me, so I will summarize them. Suppose that $N_t$ is not stochastic and $f(.)$ is a smooth function then the Taylor expansion is $$df(N_t) = f'(N_t)dN_t + \frac{1}{2}f''(N_t)(dN_t)^2 + \frac{1}{3!} f'''(N_t)(dN_t)^3 + \ldots$$ and the term $(dN_T)^2$ and higher terms are zero. Ito showed that this is not the case in the ...

11

My understanding is because the Ito's integration definition keeps the martingale property. With Brownian motion $W(t, \omega)$ defined, to define stochastic integration in a Riemann–Stieltjes style: $$\int_0^t f(t, \omega) d W(t, \omega) = \lim_{\| \Delta_n\| \to 0 } \sum_{i=1}^{n} f(\tau_i,\omega) \left ( W(t_i, \omega) - W(t_{i-1}, \omega) \right )$$ , ...

9

In fact Ito and Stratonovich calculus are both mathematically equivalent. In the following paper you can e.g. see that both derivations lead to the same result, i.e. the Black-Scholes equation: Black-Scholes option pricing within Ito and Stratonovich conventions by J. Perello, J. M. Porra, M. Montero and J. Masoliver From the abstract: Options ...

9

If you are given a diffusion process $X_t$, and a $C^{1,2}$ transformation $Y_t=f(t,X_t)$ of the process $X_t$. Then Itô's lemma gives you the SDE followed by the process $Y_t$ in terms of $dX_t$, and $dt$ and partial derivatives of $f$ up to order 1 in time and 2 in $x$. If you are given the SDE followed by $X_t$ in terms of Brownian motion, drift, and ...

6

These are all examples on Ito Formula in its general form (with quadratic variations):

5

If by 'solve' you mean how do we know that $\ln S_t$ is the right change of variable, then you can go by the following (not rigorous) line of thought: Ito's fomula suggests that given an SDE $$dX_t = \mu(X_t,t)dt+\sigma(X_t,t)dW_t$$ and a function $f(x,t)$: the SDE for the process $Y_t=f(X_t,t)$ will satisfy $$dY_t = [f_t(X_t,t) + f_x(X_t,t)\mu(X_t,t) + ... 5 In quantitative finance, we sometimes find ourselves choosing a new stochastic model for what market variables are random, and how. For example, someone might decide that they like the SDE $$dS = \mu\ S\ dt + \left( \frac{S_0}{S} \right)^{\frac32} \sigma\ S\ dW$$ because they want to capture a leverage effect. Now, this SDE ... 5 In general, if you have a process that you can write under the form F(B_t,t) where F is \mathcal{C}^{2,1} then Itô's lemma gives you the drift term and diffusion term of dF. Then if the resulting SDE has a null drift (that's where Black Scholes PDE comes from), and you get a only local martingale. For it to be a proper martingale you can look at ... 5 I think you should see the hint as follows:$$d(W_t^{n+1})=d(f(W_t))$$with$$f(x)=x^{n+1}$$Apply Ito:$$d(W_t^{n+1}) = f'(W_t)dW_t + \frac{1}{2} f''(W_t) d<W>_td(W_t^{n+1}) = (n+1) W_t^n dW_t + \frac{1}{2} n (n+1) W_t^{n-1} dt$$If you integrate, you get:$$W_{t_2}^{n+1}-W_{t_1}^{n+1}=(n+1) \int_{t_1}^{t_2} W_t^n dW_t+ ...

4

I think there is a typo in your first equation. The running variable should be $s$, as in $d\left( e^{\beta(t-s)} r(s) \right)$. Let's start with your integral. Let $R_u = e^{-\beta u} r_u$. Your integral becomes $$e^{\beta t} \int_s^t d R_u \, .$$ Recall that $dR_u = R_{u+du} - R_u$. The integral evaluates to $e^{\beta t}(R_t -R_s)$, which simplifies ...

4

Buy copies of Brent Oksendal's "Stochastic Differential Equations An Introduction with Applications" and Thomas Bjork's "Arbitrage Theory in Continuous Time." These are well written graduate level textbooks. I can't promise it will be painless, but if you want to understand continuous time derivative pricing models these are a place to start. Another ...

3

To answer the more general question that seems to be giving you trouble, Ito's lemma is the stochastic version of the chain rule of standard calculus. What is it useful for? That's like asking what the chain rule is useful for. Calculus is useful in quantitative finance, and in particular, for stochastic processes, you need to use the stochastic version ...

3

To add to the answer of TheBridge: I understand your second question in the sense if you could use Ito's lemma for all stochastic processes. This is definitely not the case: It can also be used for processes with bounded quadratic variation (e.g. Wiener process) - you should google this term or look it up in wikipedia: ...

3

The standard method to manage your kind of problem (i.e. dealing with stochastic processes that are note presented or built thanks to a Brownian motion) is to use a measure change. The power of Brownian motion is that you have a lot of representation theorems (Doob-Meyer theorem, Wold theorem, etc) that allows to (thanks to a change of measure or a ...

2

I doubt you can do this. Correction term appears in Ito because Brownian motion has infinite variation (non zero quadratic variation). In discrete and therefore finite models you cannot observe this phenomenon.

2

For Itô Processes $dX(t) = \mu(t) \mathrm{d}t + \sigma(t) \mathrm{d}W(t)$ you have the result that (under appropriate assumptions which ensure that the local martingale is a martingale, e.g. $E( (\int \sigma(t)^2 \mathrm{d}t )^{1/2} ) < \infty$, etc.): $X$ is a martingale $\Leftrightarrow$ $\mu(t) = 0$. So in order to check if a process $X$ is a ...

2

You have two processes, $X_t:=\log{\frac{f}{g}}$ and $Y_t=\frac{f}{g}$. Note, I use $\log$ for the natural logarithm. Hence we have $Y_t=\exp{(X_t)}$. Therefore, applying Itô: $$dY_t=\exp{(X_t)}dX_t + \frac{1}{2}\exp{(X_t)}d\langle X,X\rangle_t$$ Using the dynamics of $X_t$, we get ...

1

Note that the $$dX_t = b_t dt + \sigma_s dB_t$$ notation for a (local) semi-martingale $X = (X_t)_{t \in [ t_0, T]}$ is an abreviation for $$X_t = X_{t_0} + \int _{t_0} ^t b_s~ ds + \int _{t_0} ^t \sigma_s ~dB_s$$ where $b$ and $\sigma$ can be for example of the form $b_s = b(\omega, s, X_s)$ and $\sigma_s = \sigma(\omega, s, X_s)$ under condition that ...

1

If we have some function $f(a,b,c,...)$, where $a,b,c,...$ can be stochastic or otherwise, then Ito's lemma is used to find $df(a,b,c,...)$. 1) You can simply do raw Monte Carlo. Consider a contingent claim maturing in $6$ months. Then for each $i$-th simulation you can calculate: $S(T)_i = S(t)e^{(r-q-\frac12 \sigma^2)0.5 + \sigma \sqrt{0.5}z_i)}$ ...

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