8) Humboldt University Berlin

Details of open position

Scientific Staff

Prof. Dr. Nikolaus Hautsch

Nikolaus Hautsch graduated in economics from the University of Konstanz and holds a Ph.D. in economics (University of Konstanz). In 2004, he became assistant professor at the Department of Economics, University of Copenhagen. Since 2005 he has been associate professor at the Department of Economics, University of Copenhagen.

Expertise of the Team

The team consists of members from the Department of Economics (Hautsch, Juselius and Nielsen) and from the Department of Applied Mathematics and Statistics (Poulsen) in co-operation with K. M. Rasmussen from the Technical University of Denmark. The group has a strong expertise in financial econometrics, cointegration analysis, mathematical finance as well as numerical optimization techniques in finance. Hautsch has gained expertise in the field of financial high-frequency data and is working on dynamic duration and intensity models as well as latent factor models (Hautsch, 2004). Recent research focuses on the specification and estimation of computationally intensive dynamic latent factor models for high-frequency data (Bauwens and Hautsch, 2003 and Hautsch, 2005). Juselius has an outstanding research expertise in the empirical analysis of cointegrated VAR models (Juselius and Toro, 2005) and is presently completing a book on ”The Cointegrated VAR model: Methodology and Applications” (Oxford University Press, 2006). She was ranked as Nr. 8 among the most cited economists in the world in the nineties. Nielsen is working on vector autoregressive (VAR) models for cointegrated time series. The research includes analyses of the robustness of estimation results, e.g. the sensitivity to outlying observations (Nielsen, 2004). Nielsen has also contributed to the applied and theoretical research on cointegration for time series integrated of order two (I(2)). Current research includes applications of models featuring non-linear equilibrium correction, where maximization of the likelihood function is computationally demanding. Poulsen and Rasmussen are working on the application of modern optimization techniques from operations
research and stochastic programming to complex financial decision problems. Particular focus has been given to optimal mortgage choice (Nielsen and Poulsen, 2004).


  • N. Hautsch: “Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities”, Journal of Financial Econometrics, 2003.
  • K. Juselius and J. Toro: “The effect of joining the EMS. Monetary transmission mechanisms in Spain”, Journal of International Money and Finance, 2005.
  • H. B. Nielsen: “Cointegration Analysis in the Presence of Outliers”, The Econometrics Journal, 2004.
  • S. Nielsen and R. Poulsen: “A Two-Factor, Stochastic Programming Model of Danish Mortgage-Backed Securities”, Journal of Economic Dynamics and Control, 2004.

Expertise in Training Young Scientists

Hautsch has been a member of one Ph.D. committee and taught two Ph.D. courses during the last two years. Juselius supervised 6 Ph.D. students and taught 6 Ph.D. courses during the last five years. Nielsen is currently supervising three Ph.D. students. He has lectured at The Econometrics Summer School (Department of Economics) on cointegration for the last three years. Poulsen supervised one Ph.D. student and has taught one Ph.D. courses per year during the last five years.

Links within the Network

There exist informal links to Peter Winker and Aleksander Welfe who has been working on similar problems as Katarina Juselius. Katarina Juselius has been invited speaker at the Macromodels’ conference in 2003.

Role of the Research Team

The research team at the University of Copenhagen will concentrate on applications of new and alternative optimization methods in financial econometrics, cointegration analysis as well as mathematical finance. One focus will be on the application of these techniques to the modelling of financial transaction data where one is confronted with a huge amount of data. Another focus is the use of stochastic programming for mortgage choice problems. Ongoing research looks at technical issues (efficient trees and arbitrage-free discretization when time-steps are large and non-uniform), questions of optimal security design and the effects of non-standard risk factors (house-prices, labour income, and tax risk). Finally, new optimization methods will be applied in the context of nonlinear cointegration models.