In this chapter, we explored linear time series models for the univariate case of individual series as well as multivariate models for several interacting series. We encountered applications that predict macro fundamentals, models that forecast asset or portfolio volatility with widespread use in risk management, as well as multivariate VAR models that capture the dynamics of multiple macro series, as well as the concept of cointegration, which underpins the popular pair-trading strategy.

Similar to the previous chapter, we saw how linear models add a lot of structure to the model, that is, they make strong assumptions that potentially require transformations and extensive testing to verify that these assumptions are met. If they are, model-training and -interpretation is straightforward, and the models provide a good baseline case that more complex models may be able to improve on, as we will see in the following chapters.