The chain rule for the Stratonovich integral

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In summary, the chain rule for the Stratonovich integral provides a mathematical framework for differentiating composite stochastic processes. It establishes how to compute the derivative of a function of a stochastic process by linking it to the underlying processes, accounting for their stochastic nature. This rule is essential in stochastic calculus, particularly in applications involving Itô processes, as it helps in understanding how changes in one process affect another through their joint behavior. The chain rule differs from the Itô calculus, emphasizing the need to consider the geometric interpretation of stochastic integrals.
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Homework Statement
Verify the chain rule holds for the 1-d Stratonovich integral.
Relevant Equations
$$df(X_t)=f'(X_t)\circ dX_t$$

where ##X_t## solves ##dX_t=bdt+\sigma\circ dW_t##.
Professor says one way to do this is to convert the equations to Itô form and back.

##dX_t=bdt+\sigma\circ dW_t## converted to Itô's SDE is
\begin{align*}
dX_t=&\left(b+\frac{1}{2}\sigma\frac{\partial}{\partial x}\sigma \right)dt+\sigma dW_t.
\end{align*}
We use Itô's formula to compute ##d(f(X_t)).##
\begin{align*}
d(f(X_t))=& \left(\frac{\partial}{\partial t}f+\left(b+\frac{1}{2}\sigma\frac{\partial}{\partial x}\sigma\right)\frac{\partial}{\partial x}f+\frac{\sigma^2}{2}\frac{\partial^2}{\partial x^2}f\right)dt+\sigma\frac{\partial}{\partial x}fdW_t\\
=& \left(\left(b+\frac{1}{2}\sigma\frac{\partial}{\partial x}\sigma\right)f'+\frac{\sigma^2}{2}f''\right)dt + \sigma f'dW_t.
\end{align*}
This is an SDE that's solved by ##f(X_t)##, so it can be converted to the Stratonovich form. To do this, we can see that the ##\frac{\sigma^2}{2}f''=\frac{\sigma}{2}\frac{\partial}{\partial x}(\sigma f') ## term is the additional 'drift' term that disappears in the Stratonovich form.
\begin{align*}
d(f(X_t))=&\left(b+\frac{1}{2}\sigma\frac{\partial}{\partial x}\sigma\right)f'dt+\sigma f'\circ dW_t\\
=& \left(\left(b+\frac{1}{2}\sigma\frac{\partial}{\partial x}\sigma\right)dt+\sigma \circ dW_t\right)f'\\
=& f' dX_t.
\end{align*}

I'm unsure how to get ##f'\circ dX_t## because I don't think I can factor out ##f'## in the second to last line. Since we have the Stratonovich integral symbol ##\circ##, what is the correct step to take? The professor says we can assume that ##f## is invertible. Also she said remember that ##\frac{\partial}{\partial f}=\left(\frac{\partial f}{\partial x}\right)^{-1}\frac{\partial}{\partial x}##, which I'm guessing means an operation that takes ##f## into ##1## . And I didn't use either of these hints the professor gave me, so I'm suspicious.
 
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well. I can see that I made several mistakes already. I'll post again after spending time with a pen and paper. thanks
 

FAQ: The chain rule for the Stratonovich integral

What is the chain rule for the Stratonovich integral?

The chain rule for the Stratonovich integral is a mathematical formula that allows us to differentiate a function of a stochastic process that is described by a Stratonovich integral. It states that if you have a stochastic process \( X_t \) and a twice continuously differentiable function \( f \), then the Stratonovich integral of \( f(X_t) \) can be expressed in terms of the derivatives of \( f \) and the stochastic process itself.

How does the chain rule for the Stratonovich integral differ from that of the Itô integral?

The main difference lies in the treatment of the stochastic calculus. The chain rule for the Itô integral involves the Itô lemma, which incorporates the second derivative of the function due to the quadratic variation of the process. In contrast, the Stratonovich integral chain rule does not include this second derivative term because it is based on a different interpretation of stochastic calculus that aligns more closely with classical calculus.

What are the conditions for applying the chain rule for the Stratonovich integral?

To apply the chain rule for the Stratonovich integral, the function \( f \) must be at least twice continuously differentiable, and the stochastic process \( X_t \) should be adapted to the filtration of the underlying probability space. Additionally, the process should have continuous paths to ensure that the integrals are well-defined.

Can you provide an example of the chain rule for the Stratonovich integral?

Certainly! Suppose \( X_t \) is a Brownian motion and \( f(x) = x^2 \). According to the chain rule for the Stratonovich integral, we have: \[d f(X_t) = f'(X_t) dX_t + \frac{1}{2} f''(X_t) dt.\]Here, \( f'(x) = 2x \) and \( f''(x) = 2 \), so applying the chain rule gives:\[d f(X_t) = 2X_t dX_t + dt.\]This illustrates how the chain rule incorporates both the stochastic differential and a deterministic term.

What are some applications of the chain rule for the Stratonovich integral?

The chain rule for the Stratonovich integral is widely used in various fields such as finance, physics, and engineering, especially in modeling systems influenced by random noise. It allows for the analysis of stochastic differential equations (SDEs) and is particularly useful in the formulation of models that require a more intuitive treatment of noise, such as in the modeling of financial derivatives or in the analysis of dynamical systems subject to random perturbations.

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