Syllabus

LVTyp
PI 
Semesterstunden
2 
Unterrichtssprache
Englisch
Wochentag  Datum  Uhrzeit  Raum 

Mittwoch  08.05.2019  14:00  15:30  TC.5.01 
Mittwoch  08.05.2019  15:30  18:00  TC.4.01 
Mittwoch  15.05.2019  14:15  15:45  D5.1.001 
Donnerstag  16.05.2019  09:00  11:00  TC.5.27 
Mittwoch  22.05.2019  14:15  15:45  D5.1.001 
Donnerstag  23.05.2019  09:00  11:00  TC.3.03 
Mittwoch  29.05.2019  14:15  15:45  D5.1.001 
Mittwoch  05.06.2019  14:15  15:45  TC.2.03 
Mittwoch  05.06.2019  16:00  18:00  TC.4.01 
Donnerstag  06.06.2019  09:00  11:00  TC.4.01 
Mittwoch  12.06.2019  14:15  15:45  D5.1.001 
Donnerstag  13.06.2019  09:00  11:00  TC.4.01 
Mittwoch  19.06.2019  14:15  15:45  D5.1.001 
Freitag  28.06.2019  09:00  10:30  D3.0.225 
 Kontakt
 Inhalte der LV
 Lernergebnisse (Learning Outcomes)
 Regelung zur Anwesenheit
 Lehr/Lerndesign
 Leistung(en) für eine Beurteilung
 Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen
 Empfohlene inhaltliche Vorkenntnisse
 Erreichbarkeit des/der Vortragenden
 Sonstiges
 Literatur
 Detailinformationen zu einzelnen Lehrveranstaltungseinheiten
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 nonstationary from stationary series and how to apply unitroot 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.
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/BFE.zip. This provides the opportunity to replicate the examples on their own, prepare for homework assignments and the final exam.
Preparation: 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 below for each unit).
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. it is not recommended that group members only do parts of an assignment; 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 multiplechoice 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).
Successful completion of Mathematics I and Financial Markets and Instruments.
1 
Autor/in: Alois Geyer
Anmerkungen: Lecture notes Jahr: 2019 Prüfungsstoff: Ja Empfehlung: Unbedingt notwendige Studienliteratur für alle Studierenden Art: Skriptum 
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 emails 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 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/BFE.zip
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