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Fitting Data to Stochastic Differential Equation (ITO Process)

Posted 12 years ago
POSTED BY: Edvin Beqari
2 Replies
Posted 11 years ago

So, as I learn more about the subject - I have discovered that to fit state observations to a given stochastic differential equation:

One has to apply the Ito Lemma to the SDE in order to find the Forward Kolmogorov equation form. Then, that PDE can be solved with an initial condition using a Fourier transform, and what you get is the joint density of the state and time.

Now the algebra is not particularly fun - and it is also hard to visualize. This is the reason I hope to learn how to do this in Mathematica.

Here is a link to my other post where I ask how to solve the PDE in Mathematica:

Solve the Kolmogorov PDE

Learning something new everyday..Cheers

POSTED BY: Edvin Beqari
Posted 12 years ago

Ok so based on the vast amount of responses - I realize i did a great job on laying out the problem.

The Main question is : How do you fit data to SDEs with Mathematica which have a drift, and a diffusion coefficient that is not constant?

Here is the model again - and hopefully a bit clearer.

enter image description here

And here is a sample data - acceleration in the Y-directions:

{0.59, 0.311, 0.094, -0.063, -0.307, -0.552, -0.867, -1.144, -1.405, -1.58, -1.742, -1.753, -1.541, -1.1, -0.551, -0.012, 0.343, 0.357, 0.361, 0.283, 0.084, -0.743, 0.45, -0.482, 0.042, -0.024}

I will also like to post the link from a presentation given by Oleksandr Pavlyk. At 15:46 he presents a similar model, however he does not have much time there to go in depth. Awesome presentation though. I wish we could see more of that.

Stochastic Calculus and Applications

So, any help would be greatly appreciated. Let's make this an open conversation and if someone wants to bring his own example - that would also be very helpful.

Cheers!

POSTED BY: Edvin Beqari
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