A year ago I started studying the MSc in Financial Engineering offered by WQU and I wrote about the course here. As I am writing this post, I have now completed around 60% of the modules so I thought that an update would be helpful for prospective students.

These are the modules that I have completed:

(1) Financial Markets: this is a general introduction to financial markets and financial products. As the course caters towards students with different backgrounds, if you have studied for financial qualifications (such as CFA, FRM etc.) you will find this to be a gentle refresher. As it’s hard to test any maths skills, most of the assignments/essays will be theoretical.

(2) Econometrics: OK, you will finally get your hands dirty and start doing some work with time series. You will start with classic models such as GARCH, ARCH, ARIMA in analysing time series of end of day asset prices. Although you get the choice between R and Python, at least for this module, I strongly recommend sticking to R (lecture notes are better written for R).

(3) Discrete-time Stochastic Processes, (4) Continuous-time Stochastic Processes: these two courses in my opinion are at the heart of the programme; when it comes to asset pricing, having an understanding of stochastic processes and their properties is indispensable when deriving pricing equations of various claims. These two modules will be especially tough for candidates with a weak mathematical background. These concepts are quite abstract so they just require some time to sink in – I wouldn’t get discouraged if you see a massive knowledge gap. The assignments were focused on applying Monte Carlo methods and analytical pricing formulas on exotic derivatives. The projects from these modules were the most enjoyable so far.

(5) Computational Finance
This module builds on a lot of the concepts learned during the previous two modules by teaching students how to implement the concepts in python. It has an emphasis in applying Monte Carlo Methods in pricing exotic options and computing CVA. I found this module to be very practical.

(6) Machine Learning in Finance
They seem to cover a lot of the so called classic topics in ML: classifiers, supervised & unsupervised learning, etc. You will be using tensor-flow in training the various models, making predictions and assessing the model’s accuracy.