How Does Ito's Lemma Simplify the Stochastic Integral of W^n?

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The discussion centers on proving the Ito stochastic integral result for n > 1, specifically the equation involving the integral of W^n dW. Participants suggest applying Ito's differentiation rule to express W^n dW in terms of dW^(n+1) and W^(n-1) dt, akin to integration by parts in stochastic calculus. The conversation highlights the need to differentiate the function f(W) = W^(n+1) and correctly apply the stochastic differential equation framework. Clarifications are sought on the steps involved in proving the equation, particularly regarding the treatment of terms in the differentiation process. The thread emphasizes the importance of recognizing the relationship between stochastic differential equations and integral results in this context.
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Prove the following result for the ito stochastic integral (n>1)
∫_(t_0)^t▒〖W^n dW = 1/(n+1) (W^(n+1) (t)- 〗 W^(n+1) (t_0 ))- n/2 ∫_(t_0)^t▒〖W^(n-1 ) dt〗 Hint: apply ito differentiation rule f(W) = W^(n+1) to express W^n dW via dW^(n+1) and W^(n-1) dt (analogue of integration by parts for stochastic calculus)

=> stochastic differential equation: dW_t= A(t,W_t)dt +B(t,W_t)dW

∂f(t,W_t)= (∂f/∂t)* dt + ∂f/∂W *(dW_t) + 1/2 ∂^2/∂W^2 * (dW_t ^2)

We have f(W) = W^(n+1)
A=0 and B=1

Can some one help me after this to prove the equation
 
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ra_forever8 said:
Prove the following result for the ito stochastic integral (n>1)
∫_(t_0)^t▒〖W^n dW = 1/(n+1) (W^(n+1) (t)- 〗 W^(n+1) (t_0 ))- n/2 ∫_(t_0)^t▒〖W^(n-1 ) dt〗 Hint: apply ito differentiation rule f(W) = W^(n+1) to express W^n dW via dW^(n+1) and W^(n-1) dt (analogue of integration by parts for stochastic calculus)

=> stochastic differential equation: dW_t= A(t,W_t)dt +B(t,W_t)dW

∂f(t,W_t)= (∂f/∂t)* dt + ∂f/∂W *(dW_t) + 1/2 ∂^2/∂W^2 * (dW_t ^2)

We have f(W) = W^(n+1)
A=0 and B=1

Can some one help me after this to prove the equation

Use Ito's Lemma:
dX_t = a(X_t,t) dt + b(X_t,t) dW_t \text{ and } Y_t = f(X_t,t)\\<br /> \text{implies}\\<br /> dY_t = f_x(X_t,t) dX_t + f_t(X_t,t) dt + \frac{1}{2} f_{xx}(X_t,t) (dX_t)^2 \\<br /> = f_x(a \, dt + b\, dW) + \frac{1}{2} f_{xx} b^2 dt = \left(a\, f_x + f_t + \frac{1}{2}b^2 \,f_{xx}\right)\, dt + f_x b \,dW
Apply this to ##a = 0, b = 1, f(x) = x^{n+1}##.
 
dY_t= af_x *dt + f_t * dt +1/2 b^2 f_xx *dt + f_x *b *dW
Applying a =0 and b=1
=0+ f_t * dt+ 1/2 b^2 f_xx *dt + f_x *b *dW
with f(x) = x^(n+1)
Now differentiating in terms of x
= 0+ n(n-1)/2 * x^(n-1) * dt + (n+1)* x^n *dW (f_t * dt =0)
= n(n-1)/2 * x^(n-1) * dt + (n+1)* x^n *dW
what happens to left side term dY_t ? what to do to prove the qs?
 
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ra_forever8 said:
dY_t= af_x *dt + f_t * dt +1/2 b^2 f_xx *dt + f_x *b *dW
Applying a =0 and b=1
=0+ f_t * dt+ 1/2 b^2 f_xx *dt + f_x *b *dW
with f(x) = x^(n+1)
Now differentiating in terms of x
= 0+ n(n-1)/2 * x^(n-1) * dt + (n+1)* x^n *dW (f_t * dt =0)
= n(n-1)/2 * x^(n-1) * dt + (n+1)* x^n *dW
what happens to left side term dY_t ? what to do to prove the qs?

You should have written
dY_t \equiv d W^{n+1} = \frac{n(n+1)}{2} W^{n-1} \, dt + (n+1) W^n \, dW
When you realize that a stochastic DE is basically a shorthand notation for an integral result, you will see that you have everything you need.
 
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sorry I did my differentiation wrong
dW^(n+1) = n(n+1)/2 W^(n+1) dt + (n+1) W^n dW
dW^(n+1) = (n+1) ( n/2 *W^(n+1) dt + W^n dW)
dW^(n+1) / (n+1) = ( n/2 *W^(n+1) dt + W^n dW)
did I miss any terms? After that please?
 
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