Predicting the global minimum variance portfolio

Arbeitspapier

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the optimal GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns' conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations combined with targeting towards nonlinear shrinkage estimates of the long-run GMVP weights ensure scalability with respect to the number of assets. An empirical analysis of daily and monthly financial returns shows that the proposed models perform well in- and out-of-sample in comparison to existing approaches.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: KIT Working Paper Series in Economics ; No. 141

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Forecasting Models; Simulation Methods ; Financial Econometrics ; Portfolio Choice; Investment Decisions ; Financial Forecasting and Simulation
• Subject: Consistent loss function ; Elicitability ; Forecasting ; Generalized autoregressivescore ; Nonlinear shrinkage ; Recursive least squares

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Reh, Laura ; Krüger, Fabian ; Liesenfeld, Roman
• Event: Veröffentlichung
• (who): Karlsruher Institut für Technologie (KIT), Institut für Volkswirtschaftslehre (ECON)
• (where): Karlsruhe
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/222498
• Last update: 10.03.2025, 11:42 AM CET