Joerg Osterrieder

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Professor of Quantitative Finance, Zurich University of Applied Sciences, Switzerland

Papers since 2021

[1] Hirsa, Ali, Joerg Osterrieder, Branka Hadji Misheva, W. Cao, Yiwen Fu, Hanze Sun and K. Wong. “The VIX index under scrutiny of machine learning techniques and neural networks.” (2021).

Services to the Academic Community

• Book Series Editor "Financial Innovation and Technology" (Springer) (since November 2020)
• Editor Frontier Topics in AI in Finance "Financial Risk and Blockchain" (since November 2020)
• Editor Frontier Topics in AI in Finance and Industry (since November 2020)
• Reviewer for the Journal of Investment Strategies (since November 2020)

PhD students

• NN, Reinforcement Learning, January 2021 to December 2024, joint with Worcester Polytecnic University (WPI), US, Prof. Dr. Stephan Sturm

Teaching

Executive Education

• Blockchain, Big Data and Distributed Ledger, Certificate of Advanced Studies (CAS), Fall 2018, Spring and Fall 2019, 2020, 2021
• Machine Learning and Deep Learning in Finance, Continuing Education, Spring 2021

Courses

• Topics of Financial Engineering, Spring 2021, 2020, 2019, 2018, 2017, 2016
• Quantitative Risk Management, Spring 2021, 2017, 2016, 2015
• Mathematics of Financial Markets I, Spring 2015, 2016
• Mathematics of Financial Markets II, Fall 2015, 2016

Talks

• Fintech and Artificial Intelligence in Finance, 1st International Conference on Economics and FinTech, Athens, Greece, April 12, 2021, organized within the framework of the EU H2020 project Fintech no. 825215 (topic ICT-35-2018, Type of action: CSA) and the COST Action Fintech and AI in Finance (Action Chair: Joerg Osterrieder)

Supervision of bachelor and master theses

Since 2020:

• Chris, Bucher, Deep Reinforcement Learning for different macro-environments, BSc, Fall 2020
• Leander, Odermatt, Jetmir, Beqiraj, Deep Reinforcement Learning and trading in simulated stock movements, BSc, Fall 2020
• Antonio, Rosolia, Analyzing deep generated financial time series for various asset classes, MSc, Spring 2021
• Leander, Odermatt, Jetmir, Beqiraj, Informational gain of using global multi assets to predict S & P 500 continuous futures with Deep Reinforcement Learning, BSc, Spring 2021
• Chris, Bucher, A practical look at risk parity in futures allocation, BSc, Spring 2021
• Florian, Eckerli, GANs in Finance: Overview and practical applications, BSc , Spring 2021
• Moritz, Pfenninger, Samuel, Rikli, Bigler, Daniel Nico, Generation of financial time series on the basis of Wasserstein GAN, BSc, Fall 2020