Josef Teichmann: Catalogue data in Autumn Semester 2018

Name Prof. Dr. Josef Teichmann
FieldFinancial Mathematics
Professur für Finanzmathematik
ETH Zürich, HG G 54.2
Rämistrasse 101
8092 Zürich
Telephone+41 44 632 31 74
RelationshipFull Professor

401-3910-68LTopics in Mathematical Finance and Machine Learning Restricted registration - show details
Number of participants limited to 20.
4 credits2SJ. Teichmann
401-4611-68LRegularity Structures6 credits3VJ. Teichmann
AbstractWe develop the main tools of Martin Hairer's theory of regularity structures to solve singular stochastic partial differential equations in a pathwise way or addtionally by re-normalization techniques.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
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, M. Schweizer, M. Soner, J. Teichmann, M. V. Wüthrich
AbstractResearch colloquium
ContentRegular research talks on various topics in mathematical finance and actuarial mathematics
406-2604-AALProbability and Statistics
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.
7 credits15RJ. Teichmann
AbstractIntroduction to probability and statistics with many examples, based on chapters from the books "Probability and Random Processes" by G. Grimmett and D. Stirzaker and "Mathematical Statistics and Data Analysis" by J. Rice.
ObjectiveThe goal of this course is to provide an introduction to the basic ideas and concepts from probability theory and mathematical statistics. In addition to a mathematically rigorous treatment, also an intuitive understanding and familiarity with the ideas behind the definitions are emphasized. Measure theory is not used systematically, but it should become clear why and where measure theory is needed.
Chapters 1-5 (Probabilities and events, Discrete and continuous random variables, Generating functions) and Sections 7.1-7.5 (Convergence of random variables) from the book "Probability and Random Processes". Most of this material is also covered in Chap. 1-5 of "Mathematical Statistics and Data Analysis", on a slightly easier level.

Sections 8.1 - 8.5 (Estimation of parameters), 9.1 - 9.4 (Testing Hypotheses), 11.1 - 11.3 (Comparing two samples) from "Mathematical Statistics and Data Analysis".
LiteratureGeoffrey Grimmett and David Stirzaker, Probability and Random Processes.
3rd Edition. Oxford University Press, 2001.

John A. Rice, Mathematical Statistics and Data Analysis, 3rd edition.
Duxbury Press, 2006.