Course syllabus

Welcome to Analysis of Financial Data (Spring 2023)

This course will cover the basics of financial econometrics. There have been a few changes in the last couple of  years due to Covid 19 and the fact that I would like to used the flipped classroom in this course - but I will explain below what this means. So it is still a work in progress.....

The plan is to: 

  • Review of the linear regression model (classical, with IV, with dummy dependent variables)
  • Look at some time series models (distributed lag, ARMA, autoregressive distributed lag models) for univariate time series
  • Extend these to multivariate time series (VARs etc).
  • Introduce cointegration (in a painless way - I hope), and finally
  • investigate models in the arch family.

We will use Stata. The instruction to download and install it are available here.

Some familiarity with Stata it is welcome but not necessary. Familiarity with the linear regression model is a must.

There is no prescribed textbook, as I could not find a suitable one at the right level  covering all or most of the topics. However, references and reading will be given in each lecture.

We will have an introduction to the course on Fri Feb 16 at 10am. 

Let's go back to the flipped classroom approach. The idea behind it is as follows. While in a traditional course you attend a lecture, say, from 10:00 to 12:00 and then go home to read the references and do some practice on your own, in a flipped classroom you decided when you attend an online lecture and read the references and then you come to class to do the practice with my and your colleagues help.

There two advantages for you in doing this:

  1. You take control. You can learn at your own pace, pausing, rewinding and rewatching the online lectures.
  2. You can collaborate and learn from each other and get help practicing what you have learned.

However, to benefit from this strategy you need to prepare in advance of the class by watching online videos and doing the required readings and quizzes. The classes are going to be boring and useless if you do not study the videos in advance and if you do not participate actively asking and answering questions.

One more thing before finishing. This is not an easy course! You are expected to be familiar with the linear regression model, the jargon associated to it. And you will have to work hard. But you will gain a knowledge that is useful for your thesis and is appreciated by employers.

I hope you enjoy this course,

Giovanni Forchini

 

Course Period: 2023/02/16 - 2023/03/20
Course Start: Thursday, Thu. Feb. 16 at 10.15-11.00am on Zoom  [Zoom-link,  Meeting ID: 638 4270 8311, Passcode: 823225]

 

Schedule