Focusing on the art and science of making sense of financial data, this course teaches students how to build and analyze large databases using advanced econometric techniques.
Learning Outcomes of Financial Econometrics
- Improve your ability in applying numerical information effectively.
- Introduce the fundamental concepts in Statistics and to present certain applied tools for decision making.
- Train you in the practical application of Regression Analysis/Econometrics.
- Advance your understanding and skill set in data driven quantitative modeling.
- Prepare you for subsequent courses in the Masters of Finance curriculum.
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[MUSIC PLAYING] JOSE-LUIS GUERRERO: Welcome to Financial econometrics. You know very well that we are surrounded by data-- small data sets, large data sets, and you have to draw conclusions from the data. One of the elements is that in business, given that we are surrounded by data, we need to analyze, interpret, and visualize the data. The aim of this class is to give you tools in order to be able to do that.
Two main parts, one is called inferential statistics in which this part is going to be used to analyze estimation and hypothesis testing problem. The second part is going to be building models through multiple regression or simple regression. What is simple regression of multiple regression? You need to be the model to say something about the variable y.
The variable y is going to be fundamental. It's the problem that you want to solve. In order to create models, you need to figure out which of the variables that can help you to save something about the variable y. Financial econometrics is going to be very useful simply because we're looking at data, interpreting the data, visualizing the data are going to give you a deeper knowledge of the problem that you are considering.
Once again, we live in the world of data. And you need tools and a way of approaching the problem and also solving the problem. Financial econometrics aims at solving those problems that you are surrounded with, problem with data, small data, or big data.