Course Offerings SUMMER TERM
MASTER'S and PHD PROGRAM
Two Masters' modules (M.1 and M.2) and two PhD modules (PHD.1 and PHD.2) are offered during the summer term. M.1 is a lecture and tutorial oriented module for students who want to learn why research, innovation and teaching in the field of dynamic capital markets is one, if not the, most exciting business application for analytical minds. M.2 is a research oriented module for students well equipped with finance and statistics and Python coding experience. PHD.1 is an advanced lecture and tutorial oriented module for learning advanced modeling concepts and quant tools for research in financial economics, computational risk and asset management and AI-finance. PHD.2 is a research oriented reading group to discuss current developments in the field of deep financial economics.
MODULE (M.1): MODELING THE DYNAMICS OF FINANCIAL MARKETS
The module consists of two weekly lectures and respective tutorials
(i) Dynamic Capital Market Theory and
(ii) Essentials for Dynamic Financial Machine Learning.
There will be one modul-wide exam at the end of the semester for the 9 ECTS module.
MODULE (M.2): ADVANCED MACHINE LEARNING AND DATA SCIENCE
This module is a coding oriented research project in the interdisciplinary area of AI-Finance.
MODULE (PHD.1): DEEP FINANCE
The module consists of weekly lectures and tutorials for the following courses
(i) Advanced Concepts in Financial Engineering
(ii) Code Review for AI-Finance Applications
(iii) Topics in Deep Learning in Finance
MODULE (PHD.2): Reading Group in Deep Financial Economics
This module is a research oriented discussion group to assess the quality and impact of newly published research paper.
The following information provides further information to all courses, closely related to the information from the "Modulhandbuch".
(M.1.i) Dynamic Capital Market Theory (2/2)
The course "Dynamic Capital Market Theory" offers an introduction to the modeling of dynamic capital markets. Portfolio holdings and asset prices move dynamically across time and states. This course teaches basic financial economic thinking to help understand why this is the case and how to optimally act in such environments.
Next to the asset pricing focus, the second focus of the course is on optimal portfolio choice (robo advisory). For that, this course develops the theory of dynamic programming in discrete and continuous time and applies it to solve portfolio choice and corporate investment decisions. These concepts are key for financial engineering and the machine learning branch of Reinforcement Learning.
Students obtain proficiency in the following topics:
* Dynamic Valuation and Optimal Dynamic Asset Allocation
* Dynamic modeling in discrete time and continuous time
* Stochastic Calculus
* Markov Decision Processes and Dynamic Programming in discrete time and continuous time
* Pricing of bonds, equity, futures and options
Lectures (2 SWS) develop all concepts on the whiteboard, while exercises are solved during weekly tutorials (2 SWS).
(M.1.ii) Essentials for Dynamic Financial Machine Learning (2/2)
The course "Essentials for Dynamic Financial Machine Learning" teaches students to work with financial data, algorithms and statistical concepts.
Students are exposed to algorithms to learn key quantities of dynamic capital markets, such as time-varying risk premia, time-varying volatility and unobserved realizations of random states.
The course covers the following concepts:
* Multivariate time series modeling
* Dynamic volatility modeling
* Handling big financial data
* Estimating risk premia
* Kalman Filtering
Weekly lectures (2 SWS) develop all algorithmic material on the whiteboard. Weekly tutorials (2 SWS) solve and discuss Python solutions to selected problems.
(M.2): Advanced Machine Learning and Data Science
The course "Advanced Machine Learning and Data Science" offers students the opportunity to deep dive into research on current developments in the area of AI-Finance.
Aspects of supervised, unsupervised and reinforcement learning are combined with financial economic modeling to improve our understanding of financial markets and risk management.
Typically, students obtain access to research clusters with sizeable CPU and GPU units and starting code in Python or C++.
The complexity of topics varies to meet the students prior experience and wishes for further development.
Grading of this 9 ECTS module is based on a written scientific report, documented code and an oral presentation at the end of the semester.
Prerequisites: Students must have obtained an A or B in the module "Modeling the Dynamics of Financial Markets" or equivalent MSc courses in the field of Financial Economics and Statistics and Python programming.
(PHD.1.i) Advanced Concepts in Financial Engineering
The course covers advanced research-oriented concepts to equip students to read and understand current research in the field of quantitative financial modeling.
The course aims to strike a balance between foundational mathematical and financial economic concepts as well as new developments.
Prerequisites: (i) Students are enrolled as PhD students at KIT.
(PHD.1.ii) Code Review for AI-Finance Applications
This course focuses on reviewing published research code on current topics in AI-Finance.
Prerequisites: (i) Students are enrolled as PhD students at KIT.
(PHD.1.iii) Topics in Deep Learning in Finance
This course provides an in-depth analysis of how deep learning is being applied to tackle traditional unresolved problems in financial economics.
Prerequisites: (i) Students are enrolled as PhD students at KIT.
(PHD.2) Reading Group in Deep Financial Economics
This weekly reading group discusses current research papers in the field of financial economics and deep learning.
Prerequisites: (i) Students are enrolled as PhD students at KIT.