Martin Schweizer: Katalogdaten im Herbstsemester 2017

NameHerr Prof. Dr. Martin Schweizer
LehrgebietMathematik
Adresse
Professur für Mathematik
ETH Zürich, HG G 51.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 33 51
Fax+41 44 632 14 74
E-Mailmartin.schweizer@math.ethz.ch
URLhttp://www.math.ethz.ch/~mschweiz
DepartementMathematik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
401-0603-00LStochastik4 KP2V + 1UM. Schweizer
KurzbeschreibungDie Vorlesung deckt folgende Themenbereiche ab: Zufallsvariablen, Wahrscheinlichkeit und Wahrscheinlichkeitsverteilungen, gemeinsame und bedingte Wahrscheinlichkeiten und Verteilungen, das Gesetz der Grossen Zahlen, der zentrale Grenzwertsatz, deskriptive Statistik, schliessende Statistik, Statistik bei normalverteilten Daten, Punktschätzungen, und Vergleich zweier Stichproben.
LernzielKenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik.
InhaltEinführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik.
SkriptVorlesungsskript
LiteraturVorlesungsskript
401-3913-01LMathematical Foundations for Finance4 KP3V + 2UM. Schweizer, E. W. Farkas
KurzbeschreibungFirst introduction to main modelling ideas and mathematical tools from mathematical finance
LernzielThis course gives a first introduction to the main modelling ideas and mathematical tools from mathematical finance. It mainly aims at non-mathematicians who need an introduction to the main tools from stochastics used in mathematical finance. However, mathematicians who want to learn some basic modelling ideas and concepts for quantitative finance (before continuing with a more advanced course) may also find this of interest.. The main emphasis will be on ideas, but important results will be given with (sometimes partial) proofs.
InhaltTopics to be covered include

- financial market models in finite discrete time
- absence of arbitrage and martingale measures
- valuation and hedging in complete markets
- basics about Brownian motion
- stochastic integration
- stochastic calculus: Itô's formula, Girsanov transformation, Itô's representation theorem
- Black-Scholes formula
SkriptLecture notes will be sold at the beginning of the course.
LiteraturLecture notes will be sold at the beginning of the course. Additional (background) references are given there.
Voraussetzungen / BesonderesPrerequisites: Results and facts from probability theory as in the book "Probability Essentials" by J. Jacod and P. Protter will be used freely. Especially participants without a direct mathematics background are strongly advised to familiarise themselves with those tools before (or very quickly during) the course. (A possible alternative to the above English textbook are the (German) lecture notes for the standard course "Wahrscheinlichkeitstheorie".)

For those who are not sure about their background, we suggest to look at the exercises in Chapters 8, 9, 22-25, 28 of the Jacod/Protter book. If these pose problems, you will have a hard time during the course. So be prepared.
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 KP1KP. Cheridito, P. Embrechts, M. Schweizer, M. Soner, J. Teichmann, M. V. Wüthrich
KurzbeschreibungResearch colloquium
Lernziel
InhaltRegular research talks on various topics in mathematical finance and actuarial mathematics
406-2284-AALMeasure and Integration
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
6 KP13RM. Schweizer
KurzbeschreibungIntroduction to the abstract measure theory and integration, including the following topics: Lebesgue measure and Lebesgue integral, Lp-spaces, convergence theorems, differentiation of measures, product measures (Fubini's theorem), abstract measures, Radon-Nikodym theorem, probabilistic language.
LernzielBasic acquaintance with the theory of measure and integration, in particular, Lebesgue's measure and integral.
Literatur1. Lecture notes by Professor Michael Struwe (http://www.math.ethz.ch/~struwe/Skripten/AnalysisIII-SS2007-18-4-08.pdf)
2. L. Evans and R.F. Gariepy "Measure theory and fine properties of functions"
3. Walter Rudin "Real and complex analysis"
4. R. Bartle The elements of Integration and Lebesgue Measure
5. P. Cannarsa & T. D'Aprile: Lecture notes on Measure Theory and Functional Analysis. http://www.mat.uniroma2.it/~cannarsa/cam_0607.pdf