Proof: How do we use Ito's formula

In summary: The answer to your question is NO. If f is a C^2 function, and W(t) is a standard Wiener process, the Ito formula says: for Y(t) = f(W(t)) we have \displaystyle dY = f'(W) dW + \frac{1}{2} f''(W) dt Apply this to f(W) = W^n and use the fact that {W(t)} is non-anticipatory (i.e., events in {W(s), s > t} are independent of those in {W(s), s <= t}) to simplify the expectation E[d(W(t
  • #1
squenshl
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4

Homework Statement


For all integers n >= 1 define zn = E[W(1)]n. Use Ito's formula to prove that zn+2 = (n+1)zn. Compute zn for all integers n >= 1. z = mu.


Homework Equations





The Attempt at a Solution


How do we use Ito's formula, do we use it directly?
 
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  • #2


It looks like you're doing some work in stochastic here, but it's impossible to tell what you're trying to do. Could you define a few more things?

I assume E[] is the expectation, what is W(1)?
 
  • #3


Sorry.
I have proved the Ito formula for the special case W(1)3 = 3*integral from 0 to 1 of W(1)2 dW(t) + 3*integral from 0 to 1 of W(1) dt
 
  • #4
I was told to try doing it by induction but I have never done induction before. Please help.
 
  • #5
squenshl said:

Homework Statement


For all integers n >= 1 define zn = E[W(1)]n. Use Ito's formula to prove that zn+2 = (n+1)zn. Compute zn for all integers n >= 1. z = mu.


Homework Equations





The Attempt at a Solution


How do we use Ito's formula, do we use it directly?

If by {W(t)} you mean a standard Wiener process with W(0)=0, then calculation of E[W(1)^n] does not need Ito's formula (although you could use Ito, but that would be doing it the *hard* way). Your problem, as written, does not involve stochastic integration, but is just a simple problem in elementary probability theory! Think about the nature of W(t): what is its probability distribution for any fixed value of t?

RGV
 
  • #6
Both the problem and the title of the original post say to use Ito's formula, so we should consider how to do this.

Does [itex] E(W(1))^n [/itex] mean [itex] E(W(1)^n) [/itex]

I'm not an expert on the stochastic calculus. So let me ask if [itex] E(W(t)^n) [/itex] , as a function of T, is equal to [itex] \int_0^T W(t)^n dW_t [/itex]? If so, perhaps section 1.8 of these class notes is relevant: http://www.google.com/url?sa=t&sour...sg=AFQjCNG1sNKK8JK-WNvfqVq8MHAz6dXmug&cad=rja
 
  • #7
Stephen Tashi said:
Both the problem and the title of the original post say to use Ito's formula, so we should consider how to do this.

Does [itex] E(W(1))^n [/itex] mean [itex] E(W(1)^n) [/itex]

I'm not an expert on the stochastic calculus. So let me ask if [itex] E(W(t)^n) [/itex] , as a function of T, is equal to [itex] \int_0^T W(t)^n dW_t [/itex]? If so, perhaps section 1.8 of these class notes is relevant: http://www.google.com/url?sa=t&sour...sg=AFQjCNG1sNKK8JK-WNvfqVq8MHAz6dXmug&cad=rja

The answer to your question is NO. If f is a C^2 function, and W(t) is a standard Wiener process, the Ito formula says: for [tex] Y(t) = f(W(t))[/tex] we have
[tex] \displaystyle dY = f'(W) dW + \frac{1}{2} f''(W) dt [/tex]
Apply this to f(W) = W^n and use the fact that {W(t)} is non-anticipatory (i.e., events in {W(s), s > t} are independent of those in {W(s), s <= t}) to simplify the expectation E[d(W(t)^n)] = dE[W(t)^n] and get an ODE connecting Fn(t) to F_{n-2}(t), where Fk(t) = E[W(t)^k]. Then use the known values of F1(t) and F2(t) to get all the Fn(t), at least in principle. As I said, this is doing it the hard way.

RGV
 

FAQ: Proof: How do we use Ito's formula

What is Ito's formula?

Ito's formula is a mathematical formula used in stochastic calculus to find the differential of a function of a stochastic process. It is often used in finance and other fields to model the behavior of systems with random variables.

How do we use Ito's formula?

To use Ito's formula, we first need to have a stochastic process and a function of that process. The formula involves taking the partial derivative of the function with respect to time and the stochastic process, and then adding a correction term involving the second derivative of the function. This allows us to find the differential of the function, which can then be used in various applications, such as solving stochastic differential equations.

What are some common applications of Ito's formula?

Ito's formula has many applications in fields such as finance, physics, and engineering. In finance, it is often used to model the behavior of stock prices and other financial assets. In physics, it can be used to model Brownian motion and other stochastic processes. In engineering, it can be used to analyze systems with random variables, such as the reliability of a machine or the failure rate of a system.

Are there any limitations to using Ito's formula?

Yes, there are some limitations to using Ito's formula. It is only applicable to processes that are continuous and have continuous first and second derivatives. It also assumes that the stochastic process is "smooth" and does not have sudden jumps or discontinuities. Additionally, Ito's formula may not always provide an exact solution, and some approximations may be necessary.

Can Ito's formula be extended to higher dimensions?

Yes, Ito's formula can be extended to higher dimensions, such as two or three dimensions. This is known as the multi-dimensional Ito's formula. It involves taking the partial derivatives of the function with respect to each dimension of the stochastic process and adding correction terms involving the second derivatives. This allows for a more general and powerful application of the formula in various fields.

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