WiSe 22/23: "Development of an App for the Valuation of Exotic Options"
While closed-form solutions still exist for the valuation of common European options in many model environments (such as the Black/Scholes model), numerical methods are often required for the pricing of exotic options. Monte Carlo simulation is very computationally intensive, but at the same time very versatile. In addition, some methods exist that aim at making the procedure more efficient. These include variance reduction techniques and stratified sampling. In this team project, an app will be programmed that can evaluate exotic options in different model environments using Monte Carlo simulation.
Users of the app should be able to make different choices regarding the option design (European, American, Asian, Bermuda, Digital, Barrier,...) and the model (Black/Scholes, Heston, Merton,...). The starting point is the pricing of an ordinary European option in the Black/Scholes model. Then, the functionality of the app can be extended step by step. For programming the app we recommend the R package shiny.
The project includes four phases: (1) foundation phase, (2) concept development phase, (3) implementation phase, and (4) practical test. First, the option pricing theory must be studied before developing a concept for the app. In the implementation phase, the app is to be programmed and designed. Finally, the app has to be tested in-depth.
Previous knowledge of derivatives and programming skills are helpful, but can also be acquired during the semester. The participants should be willing to familiarize themselves with programming with R.
WiSe 21/22: "Programming a Robo Advisor for Portfolio Selection"
Investment advisors often promise their clients particularly high returns, which they aim to achieve through the targeted selection of undervalued stocks. This promise is contrasted by a large body of academic literature, which provides evidence in numerous articles that fund managers are not able to generate above-average returns without taking excessive risks. This research finding is consistent with the theory of efficient markets. Following this principle, portfolio selection is solely about avoiding unsystematic risks. The economist and Nobel Prize winner Harry Markowitz developed a theory for this purpose, which today serves as the basis for numerous robo-advisors.
Based on the theory of Markowitz, an app is to be developed, which is to give the user a recommendation for the composition of a portfolio. The user's individual preferences (such as risk attitude, avoidance of certain asset classes or "sin stocks") should be taken into account. For programming the app we recommend the R package shiny. The project includes four phases: (1) foundation phase, (2) concept development phase, (3) implementation phase, and (4) practical test. First, Markowitz's portfolio theory will be studied before developing a concept for the app. In the implementation phase, the app is to be programmed and designed. Finally, the app must be tested thoroughly.
Participants should be familiar with the basics of finance. Programming skills are helpful, but not mandatory. However, participants should be willing to familiarize themselves with programming with R.
WiSe 20/21: "Development of a web application for the analysis of stock returns"
For qualified investment decisions, a sound analysis of equity and bond returns is essential. In practice, frequently used statistics such as variance and beta are not sufficient for risk assessment. Instead, the value of an investment depends on whether the cash flows covary with systematic factors. Unfortunately, it is usually impossible for the standard retail investor to carry out a sound analysis. Therefore, this team project aims to develop a web application that analyzes and evaluates any investment opportunity based on modern asset pricing models.
First of all, the members must make themselves familiar with modern asset pricing models. Based on this, a detailed concept for the app should be developed. In a second step, the basic functions of the app are to be programmed in R before an app environment is added using the R package Shiny. In the third step, the app is to be optimized from a user perspective. The team should work out a presentation ("pitch") and develop a video tutorial that introduces the functionality of the app.