a) Questions to be addressed

  • How well do in-sample optimized Markowitz portfolios perform compared to naive strategies?
  • Different methods are to be compared empirically using performance and risk measures.
  • Reasons for differences in the performance of different portfolio optimizations (e.g. parameter uncertainty) are to be worked out on the basis of the literature and empirically.

b) Data set to be used

  •  A data set is to be selected independently on the basis of the literature.
  •  For this purpose, stock, commodity, currency and index returns are conceivable.

c) Additional information to be processed

It is necessary to use a true out-of-sample methodology to evaluate portfolios with R: "Rolling Window" or "Expanding Window" methods should be used here. These have to be programmed using the statistical software R.




  • Introductory Statistics with R von Daalgard (in der Bibliothek verfügbar)
  • Time Series Models for Business and Economic Forecasting von Franses, van Dijk und Opschoor, Cambridge University Press (kann per Fernleihe bezogen werden)
  • Probability and Statistics with R von Ugarte, Militino und Arnholt (in der Bibliothek verfügbar)
  • Angewandte Statistik: Methodensammlung mit R vonSachs/Hedderich (in der Bibliothek verfügbar)
  • Introductory Econometrics von Wooldridge (in der Bibliothek verfügbar)
  • The R Book von Crawley (in der Bibliothek verfügbar)
  • Albrecht A., Maurer R. (2005), 2. Auflage Schäffler Poeschel, Investment- und Risikomanagement





  • DeMiguel V., Garlappi L., Uppal R. (2008), Rev. Financial Studies, Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?




Resources for getting started in R are provided by the supervisor


Academic Council
Faculty of Business and Economics
  • Room 2319 (Building J)