Josef Teichmann: Catalogue data in Spring Semester 2020

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

363-1114-00LIntroduction to Risk Modelling and Management3 credits2VB. J. Bergmann, D. N. Bresch, J. Teichmann
AbstractThis course provides an introduction to various aspects of modelling, dealing and managing risk across different industries, contexts and applications. Classes will alternate between risk professionals from industry and government and academics coming from different disciplines.
ObjectiveStudents get familiar with the building blocks of risk modelling: uncertainty, vulnerability, resilience, decision-making under uncertainty. The course looks at different approaches to modelling and dealing as well as mitigating different kind of risks in different industries and get to understand the relation to the decision-making process in business and the value chain of a company. Cases range from enterprise risk management, natural catastrophes, climate risk, energy market risk, risk engineering, financial risks, operational risk, cyber risk and more. An additional emphasis will be on the data-driven approach to smart algorithms applied to risk modelling and management. After taking this course, students should be able to demonstrate that they can identify and formulate a risk analysis problem with quantitative methods in a particular field.
ContentThe course covers the following areas:
1. Fundamentals of Risk Modelling: Probability, Uncertainty, Vulnerability, Decision-Making under Uncertainty
2. Fundamentals of Risk Management and Enterprise Risk Management
3. Risk Modelling and Management across Different areas with invited Speakers
The list of Speakers can be found here:
Lecture notesLecture notes and slides will be provided via moodle
401-0614-00LProbability and Statistics Information Restricted registration - show details 5 credits2V + 2UJ. Teichmann
AbstractEinführung in die Wahrscheinlichkeitstheorie und Statistik
Objectivea) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden

b) Probabilistisches Denken und stochastische Modellierung

c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
ContentWahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle
Lecture notesLernmaterialien sind erhältlich auf
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.
401-5820-00LSeminar in Computational Finance for CSE4 credits2SJ. Teichmann
401-5910-00LTalks in Financial and Insurance Mathematics Information 0 credits1KP. Cheridito, M. Schweizer, J. Teichmann, M. V. Wüthrich
AbstractResearch colloquium
ObjectiveIntroduction to current research topics in "Insurance Mathematics and Stochastic Finance".