So, I'm in need of some tips regarding a small project I'm doing. My goal is an implementation of a Fast Fourier Transform algorithm (FFT) which can be applied to the pricing of options.
-which FFT, there are a lot of differents Algorithms, which can be called FFT, the most famous one being Cooley-Tukey I guess.
My thoughts on this: I prefer the most simple one, since this is no thesis or a big project, just a course on Algorithms. But it has to be compatible with option pricing (in contrast with the most -well in our general literature- referenced application of images/sound processing). So it depends on the form of input that is provided (on which I need some advice). I'm familiar with the several improvements, like a Fractional FFT, mixed radix FFT etc. But these seem pretty complex and optimization/performance driven, which is not relevant for my project.
-which Pricing model:
I Guess Black Scholes is a bit too 'flat' and I am aware of the several models that emerged after BS. So with the same objectives as stated above I'd initially prefer the Heston model.
There are a lot of considerations, and truth is I just can't see the wood for the trees.
Some background info:
My background is a B.Sc in Mathematics (Theoretical), so I have some understanding of fourier transforms. Goal is a working FFT implementation for caclulating option pricing.
(!)It does not have to be the fastest (no extreme optimization). Goals are trying to understand the chosen FFT and having a real-world working application.
So could you give some advice on the choices?
I've read a lot of papers on FFT + Option pricing, say all the decent hits on googles first few pages. But those studies were written with a much 'higher' cause.