First I would question if all this mathematical stuff is really needed (I have a PhD in Statistics). There is a huge gap between the academic-financial and the real-world. Most models are mathematics pour la mathematics. There are a lot of would-be Einsteins in the field and they want to show how good they are in Ito-Calculus et al.I can not judge the quality of the distance-learning material. I have only read Daniel J.Duffy: Finite Difference Methods in financial engineering. I would give this book 2-Amazon stars. The author has put his dusted PhD-Thesis from the shelve. The thesis is obviously about hyperbolic DEs, the option-problems are parabolic. So he speaks most of the time of another problem.The presented code is terrible. Some parts are commented by the author with// This part of code is not really optimal but it worksUnfortunatly even this is not always true.D.Duffy has written also an article in Wilmotts magazine "A Critique of the Crank-Nicolson Scheme". The scheme has - as pointed out by the author - some numeric problems. But its practically a non-problem. The options-models are all junk. The numerical problems of Crank-Nicoloson are negible in comparison to the model-errors. We are not dealing with dynamic fluid problems.Furthermore one can solve the instability of Crank-Nicolson in a trivial way. Crank-Nicolson is a 1:1 combination of the Explicit and Implicit-Solution method. By changing the weight from 0.5 to e.g. 0.6 (in direction of the implicit method) the instability problems disappear. Theoretically only Crank-Nicolson has 2nd order convergence, the implicit method (and also a factor of 0.6) has 1st order. But the exact formulation is: One can only proove 1st order, in most problems the convergence is almost identical. Besides this, numerical accuracy is not a real topic in junk-models.Chrilly Donninger----- Original Message -----From: [hidden email]To: [hidden email] ; [hidden email]Sent: Friday, April 10, 2009 3:42 PMSubject: Re: [Quantlib-users] Advice requested: getting into computationalfinancewe have a range of distance learning courses that just might address your needs.regardsDaniel J. Duffy
From: ksvanhorn [mailto:[hidden email]]
Sent: Fri 10-04-2009 06:22
To: [hidden email]
Subject: [Quantlib-users] Advice requested: getting into computational finance
I'm an experienced software engineer with a B.S. in physics, a Ph.D. in
computer science, and a love of mathematics. I feel like my brain is going
to atrophy if I don't find some way to use my math skills, and I ran across
a description of computational finance in Wikipedia that sounds like it
could be just what the doctor ordered. The Wikipedia article states that
"knowledge of the C++ programming language, as well as of the mathematical
subfields of: stochastic calculus, multivariate calculus, linear algebra,
differential equations, probability theory and statistical inference are
often entry level requisites". I'm a bit rusty on my differential equations
and I lack the background in stochastic calculus, but I'm pretty strong on
everything else.
So how would you all recommend I go about getting into this field? One
constraint I face is that I can't drop everything and go back to school for
two years; I need to keep on making an income. But I'm pretty good at
studying things on my own.
-- Kevin S. Van Horn
--
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