Ito's Formula Question: Understanding g and h Functions in Ito's Formula

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In summary, the conversation discusses using Ito's formula to find the relationship between the RHS and the Ito formula, given the function F and the integral Y(t) - Y(0) = ∫0t dW(t). The formula is then used to express the RHS in terms of Riemann and Ito integrals, with the question of how to compute the values of the functions g and h.
  • #1
Gregg
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I have a question about the functions g and h in the Ito formula (below). The question is about finding

##F(Y(t))-F(Y(0)) = \sin(Y(t)) - \sin(Y(0))##

given that

## Y(t) - Y(0) = \int_0^t dW(t)##

Ito's formula:

##F(b,Y(b)) - F(b,Y(b)) = \int_a^b \frac{\partial F}{\partial t}(t,Y(t)) dt + \int_a^b (g(t) \frac{\partial F}{\partial x}(t,Y(t)) + \frac{1}{2}h^2(t) \frac{\partial^2 F }{\partial x^2} (t,Y(t)) dt + \int_a^b h(t) \frac{\partial f}{\partial x} (t,Y(t)) dW(t) ##

For example, we have

##F(t,x) = \sin(x)## so

##\frac{\partial F}{\partial t} = \frac{\partial Y}{\partial t} (t) \cos (Y(t)) ## etc.

Plugging all the values in

##\sin(Y(t)) - \sin(Y(0))=\int_0^t \frac{\partial Y}{\partial t}(t) \cos(Y(t)) dt + \int_0^t g(t) \cos(Y(t)) - \frac{1}{2}h^2(t) \sin(Y(t)) dt + \int_0^t h(t) dW(t) ##

does the ## Y(t) - Y(0) = \int_0^t dW(t)## set any extra condition on the ##h(t)## and ##g(t)## in the above expression? Or is it fine left as is, what are the two functions h,g? Are they mean and variance? This would mean that the mean of ##Y_t## is 0 and the variance is ##1##? Does this mean that the RV a corresponding g and h? how do I work this out?
 
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  • #2
Gregg said:
I have a question about the functions g and h in the Ito formula (below). The question is about finding

##F(Y(t))-F(Y(0)) = \sin(Y(t)) - \sin(Y(0))##

given that

## Y(t) - Y(0) = \int_0^t dW(t)##

Ito's formula:

##F(b,Y(b)) - F(b,Y(b)) = \int_a^b \frac{\partial F}{\partial t}(t,Y(t)) dt + \int_a^b (g(t) \frac{\partial F}{\partial x}(t,Y(t)) + \frac{1}{2}h^2(t) \frac{\partial^2 F }{\partial x^2} (t,Y(t)) dt + \int_a^b h(t) \frac{\partial f}{\partial x} (t,Y(t)) dW(t) ##

For example, we have

##F(t,x) = \sin(x)## so

##\frac{\partial F}{\partial t} = \frac{\partial Y}{\partial t} (t) \cos (Y(t)) ## etc.

Plugging all the values in

##\sin(Y(t)) - \sin(Y(0))=\int_0^t \frac{\partial Y}{\partial t}(t) \cos(Y(t)) dt + \int_0^t g(t) \cos(Y(t)) - \frac{1}{2}h^2(t) \sin(Y(t)) dt + \int_0^t h(t) dW(t) ##

does the ## Y(t) - Y(0) = \int_0^t dW(t)## set any extra condition on the ##h(t)## and ##g(t)## in the above expression? Or is it fine left as is, what are the two functions h,g? Are they mean and variance? This would mean that the mean of ##Y_t## is 0 and the variance is ##1##? Does this mean that the RV a corresponding g and h? how do I work this out?

What, exactly, do you mean by "finding" F(Y(t))? Do you want the expected value? The probability distribution? Something else?
 
  • #3
##F(Y(t))-F(Y(0)) = \sin(Y(t)) - \sin(Y(0)) ## this is the relationship between the RHS and the Ito formula which is given in terms of ## F(b,Y(b) - F(a, T(a)) ## it should read


## F(t, Y(t))-F(0, Y(0)) = \sin(Y(t)) - \sin(Y(0)) ##

I am to use the Ito formula to get an expression for the RHS in terms of Riemann and Ito integrals. My question is, once we have that form, what is the significance of the functions g,h, can their values be computed from the given information?
 

1. What is Ito's formula and why is it important in science?

Ito's formula is a mathematical tool used in stochastic calculus to calculate the derivative of a function of a stochastic process. It is important in science because many real-world phenomena, such as stock prices and weather patterns, can be modeled as stochastic processes. Thus, Ito's formula allows scientists to better understand and predict these complex systems.

2. How does Ito's formula differ from the traditional chain rule in calculus?

Ito's formula takes into account the random fluctuations in a stochastic process, whereas the traditional chain rule in calculus assumes a deterministic process. This is why Ito's formula includes an additional term called the stochastic integral, which accounts for the randomness in the system.

3. What are the main assumptions made in Ito's formula?

The main assumptions in Ito's formula are that the stochastic process is continuous and satisfies certain regularity conditions, and that the function being differentiated is twice continuously differentiable.

4. Can Ito's formula be applied to any type of stochastic process?

No, Ito's formula is specifically designed for continuous-time processes that have random fluctuations. It cannot be applied to discrete-time processes or processes with discontinuities.

5. What are some applications of Ito's formula in science?

Ito's formula has various applications in fields such as finance, physics, and engineering. It is used to model and analyze complex systems with random fluctuations, such as stock prices, particle diffusion, and heat transfer. It is also used in the development of financial derivatives and option pricing models.

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