Josef Teichmann: Katalogdaten im Frühjahrssemester 2022

NameHerr Prof. Dr. Josef Teichmann
LehrgebietFinanzmathematik
Adresse
Professur für Finanzmathematik
ETH Zürich, HG G 54.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 31 74
E-Mailjosef.teichmann@math.ethz.ch
URLhttp://www.math.ethz.ch/~jteichma
DepartementMathematik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
363-1153-00LNew Technologies in Banking and Finance3 KP2VB. J. Bergmann, P. Cheridito, H. Gersbach, P. Kammerlander, P. Mangold, K. Paterson, J. Teichmann, R. Wattenhofer
KurzbeschreibungTechnological 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.
LernzielAfter 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
InhaltThe 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.
SkriptThere will lecture slides to each section shared in advanced to each session.
LiteraturSelected readings and books are presented in each session.
Voraussetzungen / BesonderesThe 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 KP2SH. 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
KurzbeschreibungIn 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.
LernzielParticipants 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.
InhaltAcademic presentations from ETH faculty as well as external researchers.
Each seminar is followed by a Q&A session and (when permitted) a networking Apéro.
SkriptThe sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well.
LiteraturEach speaker will provide a literature review.
Voraussetzungen / BesonderesIn most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengefördert
Verfahren und Technologiengefördert
Methodenspezifische KompetenzenAnalytische Kompetenzengefördert
Entscheidungsfindunggefördert
Medien und digitale Technologiengefördert
Problemlösunggefördert
Projektmanagementgefördert
Soziale KompetenzenKommunikationgefördert
Kooperation und Teamarbeitgefördert
Kundenorientierunggefördert
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Verhandlunggefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengefördert
Kritisches Denkengefördert
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert
401-3932-DRLMachine Learning in Finance Information Belegung eingeschränkt - Details anzeigen
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 (info@zgsm.ch) with the course number. The email should have the subject „Graduate course registration (ETH)“.
2 KP3V + 1UJ. Teichmann
KurzbeschreibungThe 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.
Lernziel
Voraussetzungen / BesonderesBachelor 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 KP3V + 1UJ. Teichmann
KurzbeschreibungThe 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.
Lernziel
Voraussetzungen / BesonderesBachelor in mathematics, physics, economics or computer science.
401-5820-00LSeminar in Computational Finance for CSE4 KP2SJ. Teichmann
Kurzbeschreibung
Lernziel
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 KP1KB. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich
KurzbeschreibungForschungskolloquium
LernzielEinfuehrung in aktuelle Forschungsthemen aus dem Bereich "Insurance Mathematics and Stochastic Finance".
Inhalthttps://www.math.ethz.ch/imsf/courses/talks-in-imsf.html