This important unit provides the fundamental concepts of modelling volatility, with the main goal being to analyse and understand the stylised facts of asset returns. We learned to apply econometric methods capable of modelling financial data, and discussed some of the potential pitfalls of these techniques. The concepts were a bit challenging at times, but the coordinator and tutors delivered the material clearly and effectively. They explained the statistical theory behind the models, which helped students understand both the logic and the purpose of the work, as well as how to actually do the modelling using tools like EViews.

Lectures were theory-heavy but essential. They focused on explaining model assumptions and intuition rather than detailed examples. They usually started with the purpose of the topic, like how to estimate stock returns, followed by the theory and mathematical steps, with plenty of explanation and interpretation along the way. The lecturers would then show practical examples in different scenarios and demonstrate how to do the modeling in EViews. While not very interactive, they were clear and well-organised.

There was no attendance mark included in the overall unit score, but the content covered in tutorials was very useful and valuable. Tutorials provided more detail and practical applications that the lectures didn’t have time to cover. There wasn’t really any new material in the tutorials; instead, we went through all the weekly questions. It was helpful to prepare beforehand to see if you could solve the problems yourself or to figure out which concepts you were unsure about, but it wasn’t strictly necessary.

The material covered in lectures and tutorials was enough to prepare for the assessments, since many of the questions were very similar to the weekly tutorial problems. The only exception was Part B, which involved building a real-world portfolio of stocks and required some extra time to explore and understand the market on your own. The markers were really strict, with no marks given for partial answers or small mistakes, so you have to be very careful with wording and avoid easy errors. Preparing using lecture content and tutorial questions is recommended and the assignments themselves helped build the skills needed to do well in the unit.

The closed book exam covered all weeks of content and tutorial questions, and calculators were allowed. The exam wasn’t very difficult and was quite similar to past exams and the tutorial or assessment questions, so there were no big surprises. The exam tested both technical understanding and written interpretation, so knowing how to explain models clearly was important. However, because it was closed book, you really needed to memorise the key regression equations and understand the tests well.

Stay on top of lectures and don’t fall behind (concepts build on each other quickly). Use tutorials and assignments to practice interpreting output, not just running models. Focus on understanding why a model is used, not just how to apply it. Tutorials can be quite fast-paced as well, and with the integration of Eviews, it’s quite easy to feel lost. For the exam, make sure you truly understand all the concepts and their purposes, so you can apply them in different scenarios and know which tests and regressions to use. Being able to write out the steps and explain your reasoning clearly is also important.