Master-Abschlussarbeiten (ca. 40 Seiten):

Die Regelungen für die Bachelorarbeiten sind, unter Beachtung der unterschiedlichen Studienanforderungen auch auf die Masterarbeiten anzuwenden.

Wichtige Hinweise zur Erstellung von Arbeiten:

 Guidelines for writing a thesis or seminar paper

 Verfassen von Seminar- und Diplomarbeiten

 Halten von Vorträgen

 Der richtige Umgang mit dem Urheberrecht

Weitere Informationen zur Anmeldung von Bachelor- und Masterarbeiten finden Sie auf den Seiten des Prüfungsamtes

Assignment of Topics for Master Theses

The list of topics given below is available for Master theses to be supervised at the chair of Monetary Economics & International Finance.
Interest in specific topics should be expressed to:

Ricarda Geilenkirchen (geilenkirchen@economics.uni-kiel.de) - accompanied by an up-to-date record of current status and exam results.

1. Estimation of a Trivariate Multifractal Model of Asset Returns and Application in Risk Management*

Content: Implementation of the structure proposed in Calvet et al. (2006), p.207 to selected trivariate portfolios

Reference:

Calvet, E. et al. , Volatility Comovement: A Multifrequency approach, Journal of Econometrics 130, 2006.

Supervision: Duc Thi Luu

2. The Empirical Performance of Consumption Euler Equations

Content: A review of research on the validity of consumption Euler equations as a basic building block of macroeconomic models.

Literature:

Canzoneri, M. et al., Euler Equations and Money Market Interest Rates: A Challenge for Monetary Policy Models, Journal of Monetary Economics 54, 2007, 1863-1881

Supervisor: Duc Thi Luu

3. Evaluating Multifractal Forecasts of Asset Price Volatility*

Content: Follow the approach of Lux et al. (2014) or Lux et al. (2016), but evaluate forecasts with alternative loss functions (e.g., based on option valuation or economic utility function).

Literature:
Lux, T. et al., Forecasting Daily Variations of Stock Index returns with a Multifractal Model of Realized Volatility, Journal of Forecasting 33, 2014 532-541
Lux, T. et al., Forecasting Crude Oil Price Volatility and Value at Risk: Evidence from Historical and Recent Data, Energy Economics, in press
Gonzalez-Rivera, G. et al., Forecasting Volatility: A Reality Check Based on Option Pricing, Utility Function, Value-at-Risk, and Predictive Likelihood, International Journal of Forecasting 20, 2004,
2004 629-645

Supervisor: Duc Thi Luu

*requires a good command of econometric and statistical methodology

4. Robust Estimation of the Tail Index for Financial Returns: An Application of Recent Methodological Innovations (reserved)

Reference:

Brzezinski, M., Robust estimation of the Pareto tail index: a Monte Carlo analysis, Empirical Economics 51,2016

Application of the estimators presented in Brzezinski (2016) to a broad range of financial data (stock indices, currencies, precious metal, …) and comparison with more traditional estimators.

Supervisor: Duc Thi Luu

5. Prediction of Monetary Policy Decisions from Discrete Choice Models

The thesis should provide a survey on the methodology and results of pertinent literature.

Reference:

Pauwels, L., Vasnev, A., Forecast combination for discrete choice models: predicting FOMC monetary policy decisions, Empirical Economics 52, 2017 and literature quoted therein

Supervisor: Dr. Matthias Raddant

6. Persistence and Forecastability of Inflation Rates: An Application of the Stock-Watson (2007) Model to Recent Data

Application of the mentioned model using the estimation algorithm of Stock and Watson (2007) as well as the alternative approach of Creal (2012)

References:

Stock, J. and M. Watson, Why has U.S. inflation become harder to forecast? Journal of Money, Credit and Banking 39, 2007 Creal, D., A survey of sequential Monte Carlo methods for economics and finance, Econometrics Review 31, 2012

Supervisor: Duc Thi Luu

7. Zero-Intelligence Models of the Microstructure of Financial Markets: Model Structure and Estimation (reserved)

Reference:

Šmíd, M., Estimation of zero-intelligence models by L1 data, Quantitative Finance 16, 2016

This is probably the only attempt at estimation of the mentioned class of models so far. The thesis should provide an introduction to the models and the estimation approach proposed in this contribution. An attempt at replication with alternative data would be preferable.

Supervisor: Lutz Honvehlmann

9. Money base, reserves and money supply: Tests of the money multiplier hypothesis

Content: Empirical analysis with European data analogous to Carpenter and Demiralp (2012)

Reference:

Carpenter, S. and S. Demiralp, Money, Reserves and the Transmission of Monetary Policy: does the Money Multiplier Exist?, Journal of Macroeconomics 34, 2012

Supervisor: M. Raddant

10. Using Prospect Theory in Portfolio Optimization (reserved until 15.06.2018)

Content: Overview over the basic tenets of prospect theory, existing application to portfolio optimization and replication of available results with different/more recent data.

Reference:
Grishina, N. et al., Prospect theory-based portfolio optimization, Quantitative Finance 17, 2017

Supervisor: B. Yanovski

11. Patterns of Network Formation in the Interbank Money Market: An Application of the Exponential Random Graph Model

Content: Application of the framework explained in Lusher et al. (2013) to interbank credit relationships using the data studied in Finger and Lux (2017)

Reference:
Finger, K., & Lux, T. (2017). Network formation in the interbank money market: An application of the actor-oriented model. Social Networks, 48, 237-249.

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.

Supervisor: Dr. M. Raddant

12. Patterns of Network Formation in the Credit Network of Japan: An Application of the Exponential Random Graph Model

Content: Application of the framework explained in Lusher et al. (2013) to a comprehensive data base of credit relationships in the Japanese economy. Data is available at the Chair of Monetary Economics and International Finance

Reference:

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.

Supervisor: L. Honvehlmann

13. Patterns of Network Formation in the Credit Network of Spain: An Application of the Exponential Random Graph Model

Content: Application of the framework explained in Lusher et al. (2013) to a comprehensive data base of credit relationships in the Spanish economy. Data is available at the Chair of Monetary Economics and International Finance

Reference:

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.

Supervisor: Dr. Duc Thi Luu