Arbeitspapier
Linear statistical inference for global and local minimum variance portfolios
Traditional portfolio optimization has been often criticized since it does not account for estimation risk. Theoretical considerations indicate that estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather than their variances and covariances. This is also demonstrated by several numerical studies. The global minimum variance portfolio has been advocated by many authors as an appropriate alternative to the traditional Markowitz approach since there are no expected asset returns which have to be estimated and thus the impact of estimation errors can be substantially reduced. But in many practical situations an investor is not willing to choose the global minimum variance portfolio, especially in the context of top down portfolio optimization. In that case the investor has to minimize the variance of the portfolio return by satisfying some specific constraints for the portfolio weights. Such a portfolio will be called 'local minimum variance portfolio'. Some finite sample hypothesis tests for global and local minimum variance portfolios are presented as well as the unconditional finite sample distribution of the estimated portfolio weights and the first two moments of the estimated expected portfolio returns.
- Language
-
Englisch
- Bibliographic citation
-
Series: Discussion Papers in Statistics and Econometrics ; No. 1/07
- Classification
-
Wirtschaft
- Subject
-
Estimation risk
linear regression theory
Markowitz portfolio
portfolio optimization
top down investment
minimum variance portfolio
Portfolio-Management
Schätztheorie
Regression
Inferenzstatistik
Theorie
- Event
-
Geistige Schöpfung
- (who)
-
Frahm, Gabriel
- Event
-
Veröffentlichung
- (who)
-
University of Cologne, Seminar of Economic and Social Statistics
- (where)
-
Cologne
- (when)
-
2007
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Frahm, Gabriel
- University of Cologne, Seminar of Economic and Social Statistics
Time of origin
- 2007
Other Objects (12)
Predicting the global minimum variance portfolio
Bayesian estimation of the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
On the estimation of the global minimum variance portfolio
The performance of minimum variance portfolios in the Croatian tourism sector
Statistical Inference for the Tangency Portfolio in High Dimension
Minimum variance portfolio in ASEAN-6 stock markets diversification: A Vietnamese perspective
Statistical inference for the expected utility portfolio in high dimensions
Portfolios dominating indices: Optimization with second-order stochastic dominance constraints vs. minimum and mean variance portfolios
Long range dependence and non-parametric statistical inference for infinite-variance random functions
Probability and statistical inference, Vol. 2.. Statistical inference
Predicting the global minimum variance portfolio
Bayesian estimation of the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
On the estimation of the global minimum variance portfolio
The performance of minimum variance portfolios in the Croatian tourism sector
Statistical Inference for the Tangency Portfolio in High Dimension
Minimum variance portfolio in ASEAN-6 stock markets diversification: A Vietnamese perspective
Statistical inference for the expected utility portfolio in high dimensions
Portfolios dominating indices: Optimization with second-order stochastic dominance constraints vs. minimum and mean variance portfolios
Long range dependence and non-parametric statistical inference for infinite-variance random functions
Probability and statistical inference, Vol. 2.. Statistical inference
Predicting the global minimum variance portfolio
Bayesian estimation of the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
Dominating estimators for the global minimum variance portfolio
On the estimation of the global minimum variance portfolio
The performance of minimum variance portfolios in the Croatian tourism sector
Statistical Inference for the Tangency Portfolio in High Dimension
Minimum variance portfolio in ASEAN-6 stock markets diversification: A Vietnamese perspective
Statistical inference for the expected utility portfolio in high dimensions
Portfolios dominating indices: Optimization with second-order stochastic dominance constraints vs. minimum and mean variance portfolios
Long range dependence and non-parametric statistical inference for infinite-variance random functions