Syllabus

Titel
4659 Y2E Credit Risk
LV-Leiter/innen
Univ.Prof. Dr. Rüdiger Frey, Dipl.-Ing.Dr. Tanja Veza
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
07.02.2019 bis 24.02.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Freitag 08.03.2019 15:00 - 19:00 D4.0.127
Freitag 15.03.2019 15:00 - 19:00 D4.0.127
Montag 25.03.2019 14:00 - 18:00 D4.0.127
Freitag 29.03.2019 15:00 - 19:00 D4.0.127
Freitag 05.04.2019 15:00 - 19:00 D4.0.127
Freitag 12.04.2019 15:00 - 19:00 D4.0.127
Freitag 03.05.2019 15:00 - 19:00 D4.0.127
Freitag 10.05.2019 15:00 - 19:00 D4.0.127

Inhalte der LV

During this course, students will become acquainted with the essential models and the mathematical tools used in the  modeling of credit risk and in the pricing ofcredit derivatives. The course will cover both single name and portfolio models. 

Lernergebnisse (Learning Outcomes)

After completing this course, students should be able to:

  • recall  the most important credit derivatives and their use in financial applications;
  • reproduce and understand the mathematical structure of credit risk models
  • understand the mathematical derivation of key pricing formulas;
  • apply the main mathematical conceps needed for credit risk models
  • understand the relevance of dependence in the risk analysis of credit portfolios
  • assess the advantages and limitations of various classes of   credit risk models;

Apart from gaining concrete knowledge and skills, students will have the opportunity to exercise themselves in team work while working on the home assignments and presentationsprojects.

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

The course will be taught as a mixture of lectures introducing new material, exercises and paper presentations by (groups of) students.

Leistung(en) für eine Beurteilung

50% written final exam

50% home assignments (in groups)

For passing the course students will have to reach an overal score of at least 50% and  a minimum score of 40% in the final exam. 

Literatur

1
Titel:

McNeil, A. Frey, R., Embrechts, P. Quantitative Risk Management , second edition, Princeton University Press 2015

Lando, D., Credit Risk Modeling, Princeton University Press 2004 


Empfohlene inhaltliche Vorkenntnisse

Good knowledge in continuous time finance (eg CTFI) is required. Additional background either in continuous time finance or in quantitative risk management is helpful but not mandatory.
Zuletzt bearbeitet: 12.02.2019



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