Project Course (Master), summer term 2023

Target audience and registration

The course is only available for five students each semester of the Masters program in Economics. 6 Credits (PO 2012 and PO 2015) can be obtained. Assignments are based on the "first-come, first-serve" principle. The registration at the examination office has to be done before the early-exams-deadline.


Each student has to conduct an empirical study and write a paper of appr. 20 pages.


The chair  focuses on topics concerning financial markets, commodities and monetary economics. The chosen topic should cover one of these areas. Personal preferences and ideas are always welcomed and considered. Basic knowledge in econometrics and empirical research is mandatory. Knowledge in Excel and econometric software is beneficial.


Once the project course is successfully completed, the empirical results can serve as the basis for the Master-thesis.

Contact person

Please contact directly the tutor which offers topics concerning your interest.


Tutor: Fiona Höllmann

1. Speculation and its impact on liquidity in commodity futures markets

  • Ludwig, M. (2019), “Speculation and its impact on liquidity in commodity markets”, in: Resources Policy, Vol. 61, pp. 532-547.

2. Understanding uncertainty transfer in commodity futures markets - Investigating volatility spillovers and
     information transmission between commodities using multivariate GARCH models

  • Dhaene, G., Sercu, P., & Wu, J. (2022). “Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets”, in: Journal of Futures Markets, Vol. 42 (5), pp. 868-887.
  • Kang, S. H., McIver, R., & Yoon, S. M. (2017). „Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets”, in: Energy Economics, Vol. 62, pp. 19-32.

3. Uncertainty in stock markets - Investigating the impact on trading behaviour

  • Kostopoulos, D., Meyer, S., & Uhr, C. (2021), „Ambiguity about volatility and investor behaviour”, in: Journal of Financial Economics, in press.
  • Ellsberg, D. (1961), “Risk, ambiguity, and the Savage axioms”, in: The Quarterly Journal of Economics, Vol. 74 (4), pp. 643-669.


    Tutor: Elissa Iorgulescu

    4. Forecasting macroeconomic variables via machine learning models in data-rich environments

    • McCracken, M. W., & Ng, S. (2016), “FRED-MD: A monthly database for macroeconomic research”, in: Journal of Business & Economic Statistics, Vol. 34 (4), pp. 574-589.
    • Gu, S., Kelly, B., & Xiu, D. (2020), “Empirical asset pricing via machine learning”, in: The Review of Financial Studies, Vol. 33 (5), pp. 2223-2273.
    • Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., & Zilberman, E. (2021), “Forecasting inflation in a data-rich environment: the benefits of machine learning methods”, in: Journal of Business & Economic Statistics, Vol. 39 (1), pp. 98-119.

    5. The drivers of price volatility in commodity futures markets over time

    • Kaufmann, R. K. (2011), “The role of market fundamentals and speculation in recent price changes for crude oil”, in: Energy Policy, Vol. 39 (1), pp. 105-115.
    • Manera, M., Nicolini, M. & Vignati, I.  (2016), “Modelling futures price volatility in energy markets: Is there a role for financial speculation?”, in: Energy Economics, Vol. 53, pp. 220-229.

    6. The role of hedgers and speculators in commodity futures markets

    • Kang, W., Rouwenhorst, K. G. & Tang, K. (2020), „A tale of two premiums: The role of hedgers and speculators in commodity futures markets.”, in: The Journal of Finance, Vol. 75 (1), pp. 377-417.


    Tutor: Dimitrios Kanelis

    7. Monetary Policy: Quantifying monetary shocks

    • Ramey, V. A. (2016), „Macroeconomic Shocks and Their Propagation”, in: Handbook of  Macroeconomics, Vol. 2A, pp. 71-162.
    • Nakamura, E. & Steinsson, J. (2018), “High-Frequency Identification of Monetary Non-Neutrality: The Information Effect”, in: Quarterly Journal of Economics, Vol. 133(3), pp. 1283-1330.
    • Altavilla, C., Brugnolini, L. ,Gürkaynak, R.S. , Matto, R. & Ragusa, G. (2019), “Measuring euro area monetary policy”, in: Journal of Monetary Economics, Vol. 108, pp. 162-179.

    8. Unstructured Data in Monetary Policy and Financial Analysis

    • Loughran, T. & McDonald, B. (2011), “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks”, in: The Journal of Finance, Vol. 66(1), pp. 35-65.
    • Shapiro, A.H. & Wilson, D.J. (2022), “Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis”, in: Review of Economic Studies, Vol. 89(5), pp. 1-38.
    • Apel, M., Blix Grimaldi, M., & Hull, I. (2022): “How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC’s Minutes and Transcripts” in: Journal of Money, Credit, and Banking, Vol. 54(5), pp. 1460-1490.

    9. Economics of financial crises and credit cycles

    • Schularick, M. & Taylor, A.M. (2012), “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008”, in: American Economic Review, Vol. 102(2), pp. 1029-1061.
    • Baron, M., E. Verner & Xiong, W. (2021), “Banking Crises Without Panics", in: Quarterly Journal of Economics, Vol. 136 (1), pp. 51-113.
    • Sufi, A. & Taylor, A.M. (2022), “Financial Crises: A Survey”, in: Handbook of International Economics: International Macroeconomics, Vol. 6, pp. 291-340.