Top 10 Essential Resources for Learning Financial Econometrics. Part #4 - Time-Series Analysis

At this stage you will have covered the necessary undergraduate material for financial econometrics. The following two books specialise in time-series analysis, which is the main area of application for a quantitative trader who works on financial pricing data. Both of these books are designed either for graduate students or practitioners.

Firstly, I want to point out that this is quite an old book (almost 20 years, in fact!). Hence a lot of the current research literature has moved on. However, there is still plenty here which hasn't changed. Hamilton's book is geared up for the graduate level financial econometrician. It concentrates solely on time-series and so does not delve too deeply into simpler econometric theory.

The book begins with ARMA processes and forecasting, then consideres spectral analysis and asymptotic distribution theory. Later chapters include Bayesian methods, Kalman Filters and Cointegration. So why should you pick this book up if you have already mastered the previous content? The main benefit lies in the depth of the book and the fact that it provides the "bridge" to more advanced research literature.

In terms of audience, I would say that mathematicians will find the book relatively straightforward to progress through, whereas graduate economists might need to brush up on some of the mathematical prerequisites in order to make good progress.

Tsay's book complements the one by Hamilton rather well. Despite the fact it has extensive converage of time-series methods, it is written primarily for the practitioner. The book also manages to discuss aspects of high-frequency trading (HFT), market microstructure, risk management (VaR) and the continuous-time Black-Scholes framework for derivatives pricing.

The books spends a good deal of time considering non-linear time series and duration models, which is something not often considered in other works. The examples are carried out in the R language as well as S-PLUS, which makes it straightforward to implement some of the theory being discussed.

This book is extremely useful for practising algorithmic traders as it contains the usual group of time-series methods, such as ARIMA and GARCH, but also considers the models from the point of view of the investor trying to build successful models. This is in contrast to Hamilton's book, which is very much designed for the graduate student.