Josef Teichmann: Catalogue data in Autumn Semester 2019

Name Prof. Dr. Josef Teichmann
FieldFinancial Mathematics
Address
Professur für Finanzmathematik
ETH Zürich, HG G 54.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 31 74
E-mailjosef.teichmann@math.ethz.ch
URLhttp://www.math.ethz.ch/~jteichma
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
363-1100-00LRisk Case Study Challenge Restricted registration - show details
Limited number of participants.

Please apply for this course via the official website (Link). Once your application is confirmed, registration in myStudies is possible.
3 credits2SB. J. Bergmann, A. Bommier, S. Feuerriegel, J. Teichmann
AbstractThis seminar provides master students at ETH with the challenging opportunity of working on a real risk case in close collaboration with a company. For Fall 2019 the Partner will be Credit Suisse and the topic of cases will focus on machine learning applications in finance.
ObjectiveStudents work in groups on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar, students visit the partners’ headquarters, interact and conduct interviews with risk professionals. The final results will be presented at the partners' headquarters.
ContentGet a basic understanding of
o Risk management and risk modelling
o Machine learning tools and applications
o How to communicate your results to risk professionals

For that you work in a group of 4 students together with a Case Manager from the company.
In addition you are coached by the Lecturers on specific aspects of machine learning as well as communication and presentation skills.
Prerequisites / NoticePlease apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than September 13, 2019.
The number of participants is limited to 16.
401-4889-00LMathematical Finance Information 11 credits4V + 2UJ. Teichmann
AbstractAdvanced course on mathematical finance:
- semimartingales and general stochastic integration
- absence of arbitrage and martingale measures
- fundamental theorem of asset pricing
- option pricing and hedging
- hedging duality
- optimal investment problems
- additional topics
ObjectiveAdvanced course on mathematical finance, presupposing good knowledge in probability theory and stochastic calculus (for continuous processes)
ContentThis is an advanced course on mathematical finance for students with a good background in probability. We want to give an overview of main concepts, questions and approaches, and we do this mostly in continuous-time models.

Topics include
- semimartingales and general stochastic integration
- absence of arbitrage and martingale measures
- fundamental theorem of asset pricing
- option pricing and hedging
- hedging duality
- optimal investment problems
- and probably others
Lecture notesThe course is based on different parts from different books as well as on original research literature.

Lecture notes will not be available.
Literature(will be updated later)
Prerequisites / NoticePrerequisites are the standard courses
- Probability Theory (for which lecture notes are available)
- Brownian Motion and Stochastic Calculus (for which lecture notes are available)
Those students who already attended "Introduction to Mathematical Finance" will have an advantage in terms of ideas and concepts.

This course is the second of a sequence of two courses on mathematical finance. The first course "Introduction to Mathematical Finance" (MF I), 401-3888-00, focuses on models in finite discrete time. It is advisable that the course MF I is taken prior to the present course, MF II.

For an overview of courses offered in the area of mathematical finance, see https://www.math.ethz.ch/imsf/education/education-in-stochastic-finance/overview-of-courses.html.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
Abstract
Objective
ContentWe aim to comprehend recent and exciting research on the nature of
stochastic volatility: an extensive econometric research [4] lead to new in-
sights on stochastic volatility, in particular that very rough fractional pro-
cesses of Hurst index about 0.1 actually provide very attractive models. Also
from the point of view of pricing [1] and microfoundations [2] these models
are very convincing.
More precisely each student is expected to work on one specified task
consisting of a theoretical part and an implementation with financial data,
whose results should be presented in a 45 minutes presentation.
Literature[1] C. Bayer, P. Friz, and J. Gatheral. Pricing under rough volatility.
Quantitative Finance , 16(6):887-904, 2016.

[2] F. M. Euch, Omar El and M. Rosenbaum. The microstructural founda-
tions of leverage effect and rough volatility. arXiv:1609.05177 , 2016.

[3] O. E. Euch and M. Rosenbaum. The characteristic function of rough
Heston models. arXiv:1609.02108 , 2016.

[4] J. Gatheral, T. Jaisson, and M. Rosenbaum. Volatility is rough.
arXiv:1410.3394 , 2014.
Prerequisites / NoticeRequirements: sound understanding of stochastic concepts and of con-
cepts of mathematical Finance, ability to implement econometric or simula-
tion routines in MATLAB.
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 credits1KP. Cheridito, J. Teichmann, M. V. Wüthrich, further lecturers
AbstractResearch colloquium
Objective
ContentRegular research talks on various topics in mathematical finance and actuarial mathematics
406-2284-AALMeasure and Integration
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
6 credits13RJ. Teichmann
AbstractIntroduction 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.
ObjectiveBasic acquaintance with the theory of measure and integration, in particular, Lebesgue's measure and integral.
Literature1. 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