----- Original Message -----From: [hidden email]To: [hidden email]Sent: Friday, April 10, 2009 7:52 PMSubject: Re: [Quantlib-users] Advice requested: getting into computationalfinancewell, well, well.. there are some strong comments here.
however the advice is probably a good one ie to think a few times before jumping in.
There are many people who study quant finance but only a small proportion get jobs as a quant. This was true even before the financial crisis and is more true in present conditions.
Regarding dr. duffy's book, its a good starting point and worth the effort to go through the book. The comments by chrilly are true but can be worked around with the help of forums etc. I would ask this question "IF dr. duffy's book is not good, what are the other options". It is an art to put together code from different source to get something working. Sure there is some hardwork involved in it but thats the way it is with c++.
Just my thoughts
On Fri, Apr 10, 2009 at 11:38 AM, chrilly <[hidden email]> wrote:
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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