Paul Embrechts: Katalogdaten im Frühjahrssemester 2016
|Name||Herr Prof. Dr. Paul Embrechts|
Professur für Mathematik
ETH Zürich, HG E 65.1
|364-1058-00L||Risk Center Seminar Series |
Maximale Teilnehmerzahl: 50
|0 KP||2S||B. Stojadinovic, K. W. Axhausen, D. Basin, A. Bommier, L.‑E. Cederman, P. Embrechts, H. Gersbach, H. R. Heinimann, D. Helbing, H. J. Herrmann, W. Mimra, G. Sansavini, F. Schweitzer, D. Sornette, B. Sudret, U. A. Weidmann|
|Kurzbeschreibung||This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling and governing complex socio-economic systems, and managing risks and crises. Students and other guests are welcome.|
|Lernziel||Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models and approaches for open problems, to analyze them with computers or other means, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.|
|Inhalt||This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the seminar. Students and other guests are welcome.|
|Skript||There is no script, but the sessions will be recorded and be made available. Transparencies of the presentations may be put on the course webpage.|
|Literatur||Literature will be provided by the speakers in their respective presentations.|
|Voraussetzungen / Besonderes||Participants should have relatively good scientific, in particular mathematical skills and some experience of how scientific work is performed.|
|401-3629-00L||Quantitative Risk Management||4 KP||2V||P. Embrechts|
|Kurzbeschreibung||The aim of this course is to present a concise overview of mathematical methods from the areas of probability and statistics that can be used by financial institutions to model market, credit and operational risk. Topics addressed include loss distributions, multivariate models, dependence and copulas, extreme value theory, risk measures, risk aggregation and risk allocation.|
|Lernziel||The aim of this course is to present a concise overview of mathematical methods from the areas of probability and statistics that can be used by financial institutions to model market, credit and operational risk.|
|Inhalt||1. Risk in Perspective|
2. Basic Concepts
3. Multivariate Models
4. Copulas and Dependence
5. Aggregate Risk
6. Extreme Value Theory
7. Operational Risk and Insurance Analytics
|Skript||The course material (pdf-slides and further reading material) are available at |
in the section "Course material" (the username and password have been sent by email).
The textbook listed under "Literatur" below makes ideal background reading.
|Literatur||Quantitative Risk Management: Concepts, Techniques and Tools|
AJ McNeil, R Frey and P Embrechts
Princeton University Press, Princeton, 2015 (Revised Edition)
(For this course the 2005 first edition also suffices)
|Voraussetzungen / Besonderes||The course corresponds to the Risk Management requirement for the SAA ("Aktuar SAV Ausbildung") as well as for the Master of Science UZH-ETH in Quantitative Finance.|
|401-3919-60L||An Introduction to the Modelling of Extremes||4 KP||2V||P. Embrechts|
|Kurzbeschreibung||This course yields an introduction into the one-dimensional theory of extremes, and this both from a probabilistic as well as statistical point of view. This course can be seen as a first course on extremes, a sequel concentrating more on multivariate extremes.|
|Lernziel||In this course, students learn to distinguish between so-called normal models, i.e. models based on the normal or Gaussian distribution, and so-called heavy-tailed or power-tail models. |
They learn to do some standard modelling and data analysis for one-dimensional data. The probabilistic key theorems are the Fisher-Tippett Theorem and the Balkema-de Haan-Pickands Theorem. These lead to the statistical techniques for the analysis of extremes or rare events known as the Block Method, and Peaks Over Threshold method, respectively.
|Inhalt||- Introduction to rare or extreme events|
- Regular Variation
- The Convergence to Types Theorem
- The Fisher-Tippett Theorem
- The Method of Block Maxima
- The Maximal Domain of Attraction
- The Fre'chet, Gumbel and Weibull distributions
- The POT method
- The Point Process Method: a first introduction
- The Pickands-Balkema-de Haan Theorem and its applications
- Some extensions and outlook
|Skript||There will be no script available.|
|Literatur||At a more elementary level:|
 S.G. Coles (2001) An Introduction to Statistical Modeling of
Extreme Values. Springer.
 R.-D. Reiss and M. Thomas (1997) Statistical Analyis of
Extreme Values. Birkhaeuser.
At an intermediate level:
 J. Beirlant, Y. Goegebeur, J. Segers and J.L. Teugels (2004)
Statistics of Extremes: Theory and Applications, Wiley.
 P. Embrechts, C. Klueppelberg and T. Mikosch (1997)
Modelling Extremal Events for Insurance and Finance.
 S. I. Resnick (2007) Heavy-Tail Phenomena. Probabilistic and
Statistical Modeling. Springer.
At a more advanced level:
 L. de Haan and A. Ferreira (2006) Extreme Value Theory. An
 S. I. Resnick (1987) Extreme Values, Regular Variation,
and Point Processes. Springer.
|401-5910-00L||Talks in Financial and Insurance Mathematics||0 KP||1K||P. Embrechts, M. Schweizer, M. Soner, M. V. Wüthrich|
|Lernziel||Einfuehrung in aktuelle Forschungsthemen aus dem Bereich "Insurance Mathematics and Stochastic Finance".|