Patrick Cheridito: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Patrick Cheridito |
| Field | Insurance Mathematics |
| Address | Dep. Mathematik ETH Zürich, HG F 42.3 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 633 87 87 |
| patrick.cheridito@math.ethz.ch | |
| URL | http://www.math.ethz.ch/~patrickc |
| Department | Mathematics |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||
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| 363-1182-00L | New Technologies in Finance and Insurance | 3 credits | 2V | B. J. Bergmann, P. Cheridito, P. Kammerlander, J. Teichmann, R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||
| Abstract | Technological 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. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After taking this course, students will be able to - Understand the fundamentals of emerging technologies like supervised learning, unsupervised learning, reinforcement learning or quantum computing. - understand recent technological developments in financial services and how they drive transformation, e.g. see applications from fraud detection, credit risk assessment, portfolio optimization - reflect about the challenges of implementing machine learning in finance, e.g. data quality and availability, regulatory compliance, model interpretability and transparency, cybersecurity risks - understand the importance of continued research and development in machine learning in finance. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | Overall, emerging technologies are transforming the finance and insurance industries by improving efficiency, reducing costs, enhancing customer experiences, and facilitating innovation. Hence, the 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, banking and insurance are invited to take the course without explicit theoretical knowledge in financial economics. As the course will cover topics like machine learning, cyber security, quantum computing, 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. Invited guest speakers will contribute to the sessions. In addition, separate networking sessions will provide entry opportunities into finance and banking. Selected guest speakers will cover different application from the field of finance and insurance, e.g. - Fraud detection: Machine learning algorithms can be trained to identify unusual patterns in financial transactions, helping to detect fraudulent activities. - Credit scoring: Machine learning can be used to develop more accurate credit scoring models, taking into account a wider range of data points than traditional models. - Investment analysis: Machine learning can be used to analyze market trends, identify potential investment opportunities, and develop predictive models for asset prices. - Risk management: Machine learning can be used to model and forecast risk, helping financial institutions to manage and mitigate risk more effectively. The course is divided in sections, each covering different areas and technologies. Students are asked to solve a short in-class exam and one out of two group exercises cases. | ||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 364-1058-00L | Risk Center Seminar Series | 0 credits | 2S | H. 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, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, S. Wiemer, R. Zenklusen | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome. | ||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage. | ||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature will be provided by the speakers in their respective presentations. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Participants should have relatively good mathematical skills and some experience of how scientific work is performed. | ||||||||||||||||||||||||||||||||||||||||||||
| 401-3915-DRL | Machine Learning in Finance and Insurance  Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 | 2 credits | 2V + 1U | P. Cheridito | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces machine learning methods that can be used in finance and insurance applications. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to learn methods from machine learning that can be used in financial and insurance applications. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | Linear, polynomial, logistic, ridge and lasso regression, dimension reduction methods, singular value decomposition, kernel methods, support vector machines, classification and regression trees, random forests, XGBoost, neural networks, stochastic gradient descent, autoencoders, graph neural networks, transfomers, credit analytics, pricing, hedging, insurance claim prediction. | ||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | More information on https://people.math.ethz.ch/~patrickc/mlfi | ||||||||||||||||||||||||||||||||||||||||||||
| Literature | Matthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance. Springer. Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2021). An Introduction to Statistical Learning. Springer. Marcos Lopez de Prado (2018). Advances in Financial Machine Learning. Wiley. Marcos Lopez de Prado (2020). Machine Learning for Asset Managers. Cambridge Elements. Mario V. Wüthrich and Michael Merz (2023). Statistical Foundations of Actuarial Learning and its Applications. Springer. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires basic knowledge in analysis, linear algebra, probability theory and statistics. | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-3915-73L | Machine Learning in Finance and Insurance | 5 credits | 2V + 1U | P. Cheridito | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces machine learning methods that can be used in finance and insurance applications. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to learn methods from machine learning that can be used in financial and insurance applications. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | Linear, polynomial, logistic, ridge and lasso regression, dimension reduction methods, singular value decomposition, kernel methods, support vector machines, classification and regression trees, random forests, XGBoost, neural networks, stochastic gradient descent, autoencoders, graph neural networks, transfomers, credit analytics, pricing, hedging, insurance claim prediction. | ||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Course material is available on https://people.math.ethz.ch/~patrickc/mlfi | ||||||||||||||||||||||||||||||||||||||||||||
| Literature | Matthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance. Springer. Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2021). An Introduction to Statistical Learning. Springer. Marcos Lopez de Prado (2018). Advances in Financial Machine Learning. Wiley. Marcos Lopez de Prado (2020). Machine Learning for Asset Managers. Cambridge Elements. Mario V. Wüthrich and Michael Merz (2023). Statistical Foundations of Actuarial Learning and its Applications. Springer. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires basic knowledge in analysis, linear algebra, probability theory and statistics. | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-5910-00L | Talks in Financial and Insurance Mathematics | 0 credits | 1K | B. Acciaio, P. Cheridito, D. Possamaï, M. Schweizer, J. Teichmann, M. V. Wüthrich | |||||||||||||||||||||||||||||||||||||||||
| Abstract | Research colloquium | ||||||||||||||||||||||||||||||||||||||||||||
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| Content | Regular research talks on various topics in mathematical finance and actuarial mathematics | ||||||||||||||||||||||||||||||||||||||||||||