Why Research in Financial Econometrics Matters
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.
The main research areas covered by the chair of Empirical Finance and Econometrics are:
| 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)
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|Raum:||SMH | Semi 1.03|
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Benedikt Fritz (Econometrics)
Marcel Schliebs (Mathematics for Business and Economics)
Econometrics can be more than just numbers.
Feel free to enjoy the econometric poems collected by the interdisciplinary econometric poetry network or contribute your own piece of econometric poetry at
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.