During the last decade financial markets have been responding to fundamental regulatory reform, challenging economic conditions and new technologies. New financial products, rules and the onset of electronic trading in most markets have altered the landscape of financial markets. While only a few years ago, investors had to rely on their local exchange for trading, they now face the decision between numerous trading platforms to fulfill their needs.
In Europe, the Markets in Financial Instruments Directive (Mifid) harmonized regulation for investment services and increased the competition among different markets and trading platforms. Even more profound was the change induced by the Regulation National Market System (or Reg NMS) in the U.S. equity markets. While only years ago a huge amount of trading took place on the New York Stock Exchange's (NYSE) trading floor, the NYSE is now struggling to compete for order flow besides newly established electronic exchanges and dark pools.
Changes, however, not only occurred in stock markets. New derivative markets came into existence, such as the market for Credit Default Swaps. Widely popular in the beginning, providing the opportunity to hedge corporate credit risk, its reputation suffered from the recent financial crisis and as a result it has undergone fundamental regulatory changes. Another example of newly established markets are emission markets. Emissions trading programs such as the European Union Emissions Trading System lead to the development of spot, future and option trading exchanges for emission permits, which nowadays are one of the fastest-growing segments in financial services.
This ever-changing landscape of financial market generates the constant need of updates regarding the role of each single market and groups of trading exchanges within the market structure as a whole. At the same time the fast development of technologies regarding electronic trading lead to the availability of new data sources, which call for innovative approaches and methodologies, in order to extract valuable information from the huge amount of data generated by financial market trading.
Quantifying price discovery between international stock markets
Financial market microstructure: How does market design impact information transmission between different trading platforms?
Informational linkages between financial markets, in particular the corporate bond and credit default swap markets
High frequency trading- the impact of high frequency trading and market microstructure effects on price efficiency
Quantifying stock market risk using implied volatility measures
Methods: Intensity models for irregular spaced transaction data; modelling stock returns as mixture normal distributions; Markov switching models, stochastic trends and cointegration, vector error correction models
Machine learning in financial markets with focus on neural networks
| Quantitative Methods II - Statistical Learning and Data Analysis
| Econometrics (CME Master, GEMA Master, Bachelor)
| Mathematics for Business and Economics (Wirtschaftsmathematik; Bachelor)
| Current Issues: Market Microstructure of Financial Markets (Master)
| Quantitative Methods I: Applied Time Series Analysis (Bachelor)
| Quantitative Methods II: Quantitative Risk Management (Bachelor)
| Capital Market Theory (with Marcel Tyrell; Master)
| Advanced Time Series Econometrics (PhD Course)
| Applied Time Series Analysis (Advanced Methods Course)
| Financial Market Microstructure – Application and Theory (Advanced Methods Course)
| Foundations of Empirical Economics (Master)
| Methodenwerkstatt (with Nadine Meidert)
|Phone:||+49 7541 6009-2231|
|Fax:||+49 7541 6009-1299|
|Room:||SMH | Semi 1.03|
|Phone:||+49 7541 6009-1712|
|Phone:||+49 7541 6009-2233|
Lisa Winkler (Tutor Mathematics for Business and Economics)
Martin Haas (Student Employee, Tutor Econometrics)
Dorian Quelle (Tutor Mathematics for Business and Economics)
Jasper Brüns (Tutor Mathematics for Business and Economics)
Our Chair is part of the Cluster Computational Social Sciences.
You can find the Cluster's Homepage here
DFG Projekt "Intratages-Volatilitaetsmessung auf Aktienmaerkten"
Laufzeit: 2 Jahre. Start: Fruehjahr 2018
RTransferEntropy: Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy
We welcome students to write their thesis on topics corresponding to the chair’s research areas. For students who cannot settle on a topic we usually have some topics at hand that might be suitable for a bachelor or master thesis. It is advisable to start looking and discussing possible topics with us a few weeks prior to starting with the thesis. In general, a thesis at the Chair of Empirical Finance and Econometrics involves an empirical part- although exceptions are possible, if students want to write a theoretical thesis on a financial topic.
In order to successfully complete a thesis at our chair students should have a fundamental knowledge of and interest in financial markets, statistics and econometrics. Econometric applications are not limited to financial topics but can cover other topics in empirical economics.
Prior exposition to statistical software such as R, Gauss, Matlab, SAS, or any other program usually turns out to be very helpful! At least, students should be willing to learn how to work with statistical software during their thesis. We also recommend students to use LaTeX when writing their thesis (a LaTeX guide is available at our chair) and the language of choice is English.
Some examples of theses and research projects we have supervised in the past:
| Single- and Multi-Market Efficiency in Digital Currency
| Value-At-Risk Modelling: A Comparison of the Empirical Performance of Value-at-Risk
based on GARCH-Type Time Series Models and Implied Volatilities
| Price Efficiency in CDS Markets: Post-Event Price Drift and Investors’ Behaviour
| Decomposing Bid-Ask-Spreads: Comparing Adverse Selection Costs of the US Stock
| Machine Learning Asset Valuation
| Controlling the U.S. Stock Market: An Empirical Analysis of the Effects of Trump’s
| The Twitter Effect - Optimizing GARCH Volatility Forecasting with Investor Sentiment
| An Overview of Existing Arbitrage Strategies and an Elaboration of a Theoretical
Framework to Execute Merger Arbitrage in Option Markets
| Capital Buffers and the Lack Thereof: Did they Influence Bank-Sovereign Risk
For further questions please contact us.