Syllabus

Titel
4592 Y1P3 Optimization
LV-Leiter/innen
Univ.Prof. Dipl.Wirtsch.-Math.Dr. Birgit Rudloff
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
06.02.2019 bis 24.02.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Dienstag 05.03.2019 08:30 - 10:30 TC.3.05
Mittwoch 06.03.2019 13:00 - 15:00 TC.2.02
Dienstag 12.03.2019 08:30 - 10:30 TC.3.05
Donnerstag 14.03.2019 14:30 - 16:30 TC.3.05
Dienstag 19.03.2019 08:30 - 10:30 TC.3.05
Donnerstag 21.03.2019 14:30 - 16:30 TC.3.05
Dienstag 26.03.2019 08:30 - 10:30 TC.3.05
Donnerstag 28.03.2019 14:30 - 16:30 TC.3.05
Dienstag 02.04.2019 08:30 - 10:30 TC.3.05
Donnerstag 04.04.2019 14:30 - 16:30 TC.3.05
Dienstag 09.04.2019 09:00 - 11:00 D3.0.233
Montag 29.04.2019 13:00 - 15:00 TC.3.05
Donnerstag 02.05.2019 09:30 - 11:30 TC.2.01
Donnerstag 02.05.2019 09:30 - 11:30 TC.2.02

Inhalte der LV

In this lecture we give an introduction to important tools in optimization and convex analysis that are needed in various parts of quantitative  finance.

Part one of the lecture is devoted to unconstrained optimization problems. Part two is devoted to the solution of constrained optimization problems via Lagrange multiplier theory and methods based on calculus. The third part of the lecture deals with convex analysis and duality theory for convex optimization problems.

In order to illustrate the methods we will study several applications in economics and finance including Markowitz portfolio optimization, optimal production plans, portfolio optimization via expected utility maximization and cost-minimal superreplication.


Lernergebnisse (Learning Outcomes)

After completing this course the student will have the ability to:

  • understand and interpret classic models in financial economics that build on an optimization argument;
  • construct economic models that imply an optimizing decision maker and perform analytical and /or numerical analysis.
  • communicate and discuss possible approaches to a certain problem in class;
  • work in groups and contribute to the implementation of economic optimization models. Defend the chosen approach in class.
  • apply methods of static and dynamic optimization to questions arising in financial economics;

Regelung zur Anwesenheit

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

Lehr-/Lerndesign

This course will be taught as a combination of lectures in optimization theory and the solution of homework assignments, possibly in groups

Leistung(en) für eine Beurteilung

  • written final exam (weight: 70%)
  • 2 minitests  (weight of each test 15%)

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

  • Basic knowledge of Analysis and linear Algebra as in Mathematics I
  • An understanding of derivative pricing in one-period financial models will be helpful

Literatur

1 Autor/in: D. Bertsekas
Titel: Nonlinear Programming

Verlag: Athena Scientific Publishing
Auflage: 2nd
Jahr: 1999
Empfehlung: Referenzliteratur
Art: Buch

Erreichbarkeit des/der Vortragenden

brudloff@wu.ac.at

Sonstiges

 

Weitere Informationen

Lecture notes will be distributed during the course
Zuletzt bearbeitet: 07.11.2018



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