Useful courses?

[Gauss]

Hello,

I'd like to have some comments from you guys who are following an MFE or have work experience. I'm an undergraduate, doing applied maths, and when I choose my courses I'm motivated by their usefulness. I'm generally interested by probability and statistics.

*Seems like PDE's & numerical analysis are useful. Could you tell me why? Don't all the software computing packages already implement those techniques? Also, is a mathematical knowledge of PDEs (ie, theorems like the max-min principles, as opposed to applied side involving all the numerical methods) of any use and if yes why?

*Would you think a knowledge of wavelets and modern signal processing is useful (FFT, wavelet transform, statistical estimation, data compression)? By useful I mean, whether you use that stuff, whether in the context of financial markets or not, but outside academia.

*Is learning rigorously stochastic calculus "only" useful for building new financial models (ie, some quite theoretical work) or is it a must even for more basic things. Can it be used in another context than brownian motion & financial markets, ie FE?

Thanks

 

[doug reich]

What do you want to do? The means depend on the end goal. As you point out, there are many different applications for the subjects.

 

[Gauss]

I see myself working as a statistician. Areas such as trading strategies or risk management lookk interesting.

 

[Dave Haan]

For FE it's all about praxis: Theory and application go together like strategy and tactics (eg in chess), the more you know about the tools the better you can wield them. This holds for PDEs and SDEs in particular. It also determines how statistical estimation is approached. If you know your Fourier analysis you know how to use existing FFT packages. Data mining may be of more practical interest: see Statistics 36-350: Data Mining (Fall 2008)