Top 10 Essential Resources for Learning Financial Econometrics. Part 3 - Econometrics for Financial Engineering

After having studied a text like Brooks' above, you will be well on your way to being skilled within basic econometric theory and time-series modelling. The next step is to start delving deeper into the statistical basis for the econometric theory, so that you are completely familiar with when to be able to apply a certain technique to a particular financial situation.

I want to provide a bit of variety at this stage by suggesting that you take Eric Zivot's fantastic Financial Econometrics course at Coursera. I've mentioned this course a few times on QuantStart and in the mailing list. It is highly watchable and Professor Zivot works at a pace that is very manageable. The course is accessible to those with a basic probability and statistics background, but actually ventures into some relatively advanced material.

The course begins with a great set of lectures on asset returns. While this is considered "bread and butter" content for quantitative finance, it is great to have it summarised in one place. The lectures then concentrate on univariate and bivariate statistical distributions. Time-series concepts are covered, as are descriptive statistics. Professor Zivot also brushes up on matrix algebra along the way.

The latter half of the course is more directly applied to finance, with discussions on Monte Carlo modelling, bootstrapping, portfolio theory and risk budgeting. All in all, the course is a great way to learn some additional econometric ideas. Watching lectures makes a change from the late nights pouring through textbooks in the university library, too!

You can simply watch the video lectures or you can actively take part in the homework assignments. I suggest the latter, as the ideas are really only fully grasped after trying them out in Excel or R, which are the two tools used in the course.

Ruppert's book is extremely comprehensive in its treatment of financial data analysis. The book covers significant financial ground from basic asset returns through to GARCH, CAPM, Factor Models and Risk Management. The book is in fact the main recommended text for the Coursera course described above.

Perhaps the main benefit of the book is that it provides numerous worked examples in the R language, which gives the book an extremely practical edge not seen in many other texts. However, it does not teach R from the ground up. For that you will need another book (such as A Beginner's Guide to R by Zuur).

While it contains the "usual suspects" in an econometrics text (such as univariate and multivariate modelling, as well as time-series/forecasting), it also has chapters on Fixed Income Securities, Copulas and Resampling. If you have the time and inclination I suggest reading through the book in its entirety and carrying out all of the worked examples in R. This will give you a thorough grounding in modern econometrics and statistics as applied to financial datasets.