Josef Teichmann: Catalogue data in Spring Semester 2021

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

363-1153-00LNew Technologies in Banking and Finance3 credits2VB. J. Bergmann, P. Cheridito, H. Gersbach, P. Mangold, J. Teichmann, R. Wattenhofer
AbstractTechnological advances, digitization and the ability to store and process vast amounts of data has changed the landscape of banking and finance in recent years. This course will unpack the technologies underlying these transformations and reflect on the impacts on the financial world, covering also change management perspectives.
ObjectiveAfter taking this course, students will be able to
- Understand recent technological developments and how they drive transformation in banking and finance
- Understand the challenges of this digital transformation when managing financial and non-financial risks
- Reflect on the impacts this transformation has on workflows, agile working, project and change management
ContentThe financial manager of the future is commanding a wide set of skills ranging from a profound understanding of technological advances and a sensible understanding of the impact on workflows and business models. Students with an interest in finance and banking are invited to take the course without explicit theoretical knowledge in financial economics. As the course will cover topics like machine learning, cyber security, distributed computing, and more, an understanding of these technologies is welcomed, however not mandatory. The course will also go beyond technological advances and will also cover management-related contents. The course is divided in sections, each covering different areas and technologies. Students are asked to solve small cases in groups for each section. Invited guest speakers will contribute to the sessions. In addition, separate networking sessions will provide entry opportunities into finance and banking.

More information on the speakers and specific session can be found here: and on the moodle page.
Prerequisites / NoticeThe course is opened to students from all backgrounds. Some experience with quantitative disciplines such as probability and statistics, however, is useful.
401-2604-00LProbability and Statistics Information 7 credits4V + 2UJ. Teichmann
Abstract- Discrete probability spaces
- Continuous models
- Limit theorems
- Introduction to statistics
ObjectiveThe goal of this course is to provide an introduction to the basic ideas and concepts from probability theory and mathematical statistics. This includes a mathematically rigorous treatment as well as intuition and getting acquainted with the ideas behind the definitions. The course does not use measure theory systematically, but does point out where this is required and what the connections are.
Content- Discrete probability spaces: Basic concepts, Laplace models, random walks, conditional probabilities, independence
- Continuous models: general probability spaces, random variables and their distributions, expectation, multivariate random variables
- Limit theorems: weak and strong law of large numbers, central limit theorem
- Introduction to statistics: What is statistics?, point estimators, statistical tests, confidence intervals
Lecture notesThere will be lecture notes (in German) that are continuously updated during the semester.
LiteratureA. DasGupta, Fundamentals of Probability: A First Course, Springer (2010)
J. A. Rice, Mathematical Statistics and Data Analysis, Duxbury Press, second edition (1995)
401-3932-19LMachine Learning in Finance6 credits3V + 1UJ. Teichmann
AbstractThe course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep
networks and wavelet analysis, Deep Hedging, Deep calibration,
Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.
401-4611-21LRough Path Theory Information 4 credits2VA. Allan, J. Teichmann
AbstractThe aim of this course is to provide an introduction to the theory of rough paths, with a particular focus on their integration theory and associated rough differential equations, and how the theory relates to and enhances the field of stochastic calculus.
ObjectiveOur first motivation will be to understand the limitations of classical notions of integration to handle paths of very low regularity, and to see how the rough integral succeeds where other notions fail. We will construct rough integrals and establish solutions of differential equations driven by rough paths, as well as the continuity of these objects with respect to the paths involved, and their consistency with stochastic integration and SDEs. Various applications and extensions of the theory will then be discussed.
Lecture notesLecture notes will be provided by the lecturer.
LiteratureP. K. Friz and M. Hairer, A course on rough paths with an introduction to regularity structures, Springer (2014).
P. K. Friz and N. B. Victoir. Multidimensional stochastic processes as rough paths, Cambridge University Press (2010).
Prerequisites / NoticeThe aim will be to make the course as self-contained as possible, but some knowledge of stochastic analysis is highly recommended. The course “Brownian Motion and Stochastic Calculus” would be ideal, but not strictly required.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 credits1KB. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich
AbstractResearch colloquium
ObjectiveIntroduction to current research topics in "Insurance Mathematics and Stochastic Finance".
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
Abstract- Statistical models
- Methods of moments
- Maximum likelihood estimation
- Hypothesis testing
- Confidence intervals
- Introductory Bayesian statistics
- Linear regression model
- Rudiments of high-dimensional statistics
ObjectiveThe goal of this part of the course is to provide a solid introduction into statistics. It offers of a wide overview of the main tools used in statistical inference. The course will start with an introduction to statistical models and end with some notions of high-dimensional statistics. Some time will be spent on proving certain important results. Tools from probability and measure theory will be assumed to be known and hence will be only and occasionally recalled.
Lecture notesScript of Prof. Dr. S. van de Geer
LiteratureThese references could be use complementary sources:

R. Berger and G. Casella, Statistical Inference
J. A. Rice, Mathematical Statistics and Data Analysis
L. Wasserman, All of Statistics