After completing this course participants will

• have the ability to apply and interpret the results of regression analyses
• be familiar with key aspects relevant for the specification of a regression model
• understand the relevance and implications of various assumptions in each step of the analysis
• know why and how specific properties of regression residuals must be tested
• understand the consequences of violations of certain assumptions, and know how to account for them
• be familiar with basic definitions of financial returns, and able to derive and interpret their empirical (dynamic) properties
• know how to distinguish non-stationary from stationary series and how to apply unit-root tests
• understand the purpose and the basic principles of GARCH models, and how to estimate and test such models

Participants are required to attend each class, except for serious illness and/or important private concerns. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

The course is taught using a combination of lectures and practical examples demonstrated in class. The lectures are aimed at establishing a sound understanding of the main ideas and basic principles of econometric methods and analyses. Special emphasis is put on applications using financial data. For that purpose, practical examples will be presented in class. Data used in these examples are also available to participants and can be downloaded from https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics.zip. This provides the opportunity to replicate the examples on their own, prepare for homework assignments and the final exam.

Preparation:

This course is taught in a distant-teaching/learning format. Therefore, a comprehensive and focused preparation is essential for benefitting from and successfully passing the course.

1. Participants are expected to go watch the videos of the coursera course "Econometrics Methods and Applications" [https://www.coursera.org/learn/erasmus-econometrics] (prepared by Erasmus School of Economics) and work through the training exercises. Registration is free, you do not have to use your real name, and you don't have to provide personal details (I have registered in such a way). This gives you access to all available course materials.
2. Participants are expected to read my comments on these videos (available at https://www.wu.ac.at/fileadmin/wu/d/i/ifr/comments.Econometrics_Methods_and_Applications.by.Alois.Geyer.pdf). These comments provide important background information, that should clarify specific aspects of the videos. My comments also provide links to the pertinent sections in my lecture notes.
3. Participants are expected to have read the appropriate sections from the lecture notes https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics.pdf (required readings are specified before each unit).
4. Participants are expected to the know the contents of the course "QFin Statistics 2 Summer Semester 2023" taught by Kurt Hornik, in particular the contents of unit 6 on linear models; materials are available at: https://statmath.wu.ac.at/~hornik/QFS2/qfs2_facts.html.

During the course (weekly activities):

Participants have to do assignments based on the exercises specified in the lecture notes. The purpose of the assignments is to practice using actual data, to recall the methods' theoretical basis and assumptions, and to get acquainted with empirical evidence on financial data.

Participants have to engage in a prediction competition (Kaggle InClass). For that purpose, they have to develop a prediction model for a dataset which will be made available after the first unit. Predictions are evaluated on the basis of a holdout dataset. Participants will get feedback whenever they submit a prediction. The submission of predictions, feedback and monitoring are done via a specific website (details will be provided in the first unit). In each unit, the progress made in developing the prediction model will be discussed. I will provide feedback and make recommendations.

• 20% of the final grade is based on the final exam.
• 20% of the final grade is based on homework assignments. Homework assignments can be done in groups consisting of up to 3 members. Each member of a group must be able to explain all aspects of an assignment (i.e. group members must not only do parts of an assignment (i.e. must not share the workload); all group members should work jointly on the assignment and must take full responsibility).
• 30% of the final grade is based on the performance in short quizzes/tests done during class. These tests consist of multiple-choice questions, which are based on the topics actually covered in class. In principle, participants should be able to correctly answer the questions by actively following the presentations and discussions during class. Note that knowing formulas or commands of various software packages are not required for successfully answering the questions!
  Quizzes are executed using the "clicker" feature of Learn@WU. In order to participate in a quiz, each participant has to make sure she/he can access Learn@WU via a laptop, tablet, or smartphone.
• 30% of the final grade is based on the score obtained in the prediction competition.
• Participants may receive extra credits for valuable contributions made during class (e.g. when discussing homework assignments), but only in exceptional cases. There are no predefined criteria for obtaining such credits.

The final score is a weighted sum of the four subscores (30% clicker, 30% kaggle, 20% assignments, 20% final exam).

There are no minimum requirements w.r.t. any of the four subscores.

Grading scheme:

• 1 if final score >= 0.875
• 2 if final score < 0.875 and >=0.75
• 3 if final score < 0.75 and >=0.625
• 4 if final score < 0.625 and >=0.5
• 5 if final score < 0.5

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Participants should be familiar with the following topics on an undergraduate level:

• Mathematics (e.g. matrix algebra, polynomials, derivatives, etc.)
• Probability(e.g., distributions, conditional probability, expectation operators, etc.).
• Statistics(e.g., descriptive statistics, sampling distributions, hypothesis testing,etc.)
• Computing: Excel, EViews, R

I answer e-mails as soon as possible. Meetings in my office can be arranged via alois.geyer@wu.ac.at.

Section numbers for required readings refer to the lecture notes.

In each unit, one or several practical examples are used to demonstrate the principles and methods introduced in class.

Lecture notes: https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics.pdf

Associated files: https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics.zip

Comments on videos of the coursera course "Econometrics Methods and Applications" are available at https://www.wu.ac.at/fileadmin/wu/d/i/ifr/comments.Econometrics_Methods_and_Applications.by.Alois.Geyer.pdf.