Josef Teichmann: Catalogue data in Spring Semester 2022

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
363-1153-00LNew Technologies in Banking and Finance3 credits2VB. J. Bergmann, P. Cheridito, H. Gersbach, P. Kammerlander, P. Mangold, K. Paterson, J. Teichmann, R. Wattenhofer
AbstractTechnological advances, digitization and the ability to store and process vast amounts of data has changed the landscape of financial services in recent years. This course will unpack these innovations and technologies underlying these transformations and will reflect on the impacts on the financial markets.
ObjectiveAfter taking this course, students will be able to
- Understand recent technological developments in financial services and how they drive transformation
- 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 online quizzes and small cases 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: https://riskcenter.ethz.ch/education/lectures.html and on the moodle page.
Lecture notesThere will lecture slides to each section shared in advanced to each session.
LiteratureSelected readings and books are presented in each session.
Prerequisites / NoticeThe course is opened to students from all backgrounds. Some experience with quantitative disciplines such as probability and statistics, however, is useful but not mandatory.
364-1058-00LRisk Center Seminar Series0 credits2SH. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, D. Sornette, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
AbstractIn this series of seminars, invited speakers discuss various topics in the area of risk modelling, governance of complex socio-economic systems, managing risks and crises, and building resilience. Students, PhD students, post-docs, faculty and individuals outside ETH are welcome.
ObjectiveParticipants gain insights in a broad range of risk- and resilience-related topics. They expand their knowledge of the field and deepen their understanding of the complexity of our social, economic and engineered systems. For young researchers in particular, the seminars offer an opportunity to learn academic presentation skills and to network with an interdisciplinary scientific audience.
ContentAcademic presentations from ETH faculty as well as external researchers.
Each seminar is followed by a Q&A session and (when permitted) a networking Apéro.
Lecture notesThe sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well.
LiteratureEach speaker will provide a literature review.
Prerequisites / NoticeIn most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Method-specific CompetenciesAnalytical Competenciesfostered
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingfostered
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
401-3932-DRLMachine Learning in Finance Information Restricted registration - show details
Only for ETH D-MATH doctoral students and for doctoral students from the Institute of Mathematics at UZH. The latter need to send an email to Jessica Bolsinger (Link) with the course number. The email should have the subject „Graduate course registration (ETH)“.
2 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.
Objective
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.
401-3932-19LMachine Learning in Finance Information
Offered for the last time in its current form in the Spring Semester 2022. As of the Spring Semester 2023, "Machine Learning in Finance" will be replaced by "Mathematics for New Technologies in Finance" (same course number, 3V+1U, 4 ECTS credits).
6 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.
Objective
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
Abstract
Objective
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".
Contenthttps://www.math.ethz.ch/imsf/courses/talks-in-imsf.html