Seminar: Investment Case Studies (3 ECTS)
Students team up in a group to solve an investment related real-world case study. This seminar is ideally suited for students who want to deepen and apply their knowledge from classes like investments, statistics, OR or information science. Some case studies provide students with an opportunity to implement statistical investment concepts using financial software; while other case studies are more qualitative.
Bachelor Thesis Seminar (0 ECTS)
Students who write a bachelor thesis at the Chair present their findings and state open questions on a regular basis. All Bachelor thesis writers are invited to participate to learn from the research of their fellow students and to benefit from the professor's and PhD student's feedback.
Lecture: Computational Risk and Asset Management 1 (4.5 ECTS, 2/1) ExecSummary_CRAM_I.pdf
Linear multifactor models are the bread and butter of the financial industry. Such models are used for predicting returns and risks of different asset classes. They also build the basis for performance measurement and all types of smart beta investment strategies. This course provides an especially easy access to the topic as all of the empirical finance concepts rely only on simple least-squares methods. No numerical optimization is necessary for this course.
The course structure is as follows: We show how to solve a strategic asset allocation problem and present prominent special case solutions, such as equal-weight, risk parity etc portfolios. We then move on to learn simple, yet state-of-the-art forecasting methods to forecast expected returns, risk and conditional covariation of different assets. By doing so, we will study concepts such as ARMA-GARCH modeling, Impulse Response Analysis, Variance Decomposition, Principal Component Analysis. Once equipped with these empirical tools we move on to set-up and estimate several linear multi-factor models for equity, bond and volatility assets. We will also discuss and evaluate the methods and punchlines of related research papers.
During the tutorials (Python programming labs), students obtain an introduction into financial software engineering. This introduction does NOT assume any prior programming knowledge. Each tutorial hands-out a set of Python code that is useful for solving problems from the lecture. Students are then asked to further develop the code. By the end of the course, students will feel comfortable using Python to solve any type of (linear) financial problem.
This course should be attended by every student, regardless of whether they want to work in finance/consulting/fin-tech or whether they simply have to invest for retirement.
Lecture: Computational Risk and Asset Management 2 (4.5 ECTS, 2/1)
Financial markets are inherently nonlinear and non-Gaussian. Modeling these two key characteristics requires the need of non-linear estimation techniques such as generalized Maximum Likelihood and the Expectation Maximization algorithm. This course is suited for every Master KIT student and teaches investment and risk management intuition and tools that go beyond the quantitative undergraduate and MBA level.
The first two weeks of the course re-visit the same linear problems from "Computational Risk and Asset Management 1", but importantly, re-estimates these models with generalized Maximum Likelihood techniques. This didactical approach allows us to gently introduce concepts such as nonlinear optimization, computation of standard errors, efficiency of estimation techniques. We then move on to introduce nonlinear valuation models for fixed-income, equity and option markets. Students learn how to set-up the valuation models (based on risk-neutral and physical valuation) and how to empirical estimate key parameters of the models. We then introduce nonlinear and non-Gaussian State-Space-Models and apply these to bond, equity and option products. Students learn how to use Expectation Maximization algorithms to filter the unobserved states based on noisy observations; a tool that is well known in engineering and science. Last not least, students are introduced to modern macro-finance valuation concepts to model the nonlinear dynamic system of the economy, monetary policy, bond, equity and option markets.
During the tutorials (Python programming labs), students deepen their financial software engineering skills. Prior programming knowledge equivalent to "Computational Risk and Asset Management 1" is assumed. Each tutorial hands-out a set of Python code that is useful for solving problems from the lecture. Students are then asked to further develop the code. By the end of the course, students will feel comfortable using Python to solve any type of non-linear and Non-Gaussian financial problem.
Seminar: Applied Risk and Asset Management (3 ECTS)
Students will work on a quantitative problem related to risk and asset management. This seminar is ideally suited for students who want to deepen and apply their statistics / programming skills and knowledge about financial markets. Industry-relevant problems will be solved with financial data and modern statistical tools in close collaboration with a supervisor. Topics which students solved in the past include the option-based pricing of dividends during the Euro crisis, the estimation of risk neutral moments with high-frequent data and the application of a particle filter to estimate stochastic volatility. The current topics will be presented during the first meeting. The seminar consists of an introductory meeting, biweekly meetings with the advisor, an intermediate meeting to present the current progress, a final presentation and a written seminar thesis.
Master and PhD Thesis Seminar (0 ECTS)
Students who write a master or PhD thesis at the Chair present their findings and open questions on a weekly basis. All thesis writers are invited to participate to learn from the research of their fellow students and to benefit from the professor's and PhD student's feedback.