Title: Dynamic modeling of the global minimum variance portfolio weights
Authors: Fabian Krueger - Heidelberg Instititute for Theoretical Studies gGmbH (Germany)
Roman Liesenfeld - University of Cologne (Germany)
Laura Reh - University of Cologne (Germany) [presenting]
Abstract: A novel dynamic approach is proposed to forecast the weights of the global minimum variance portfolio (GMVP). We exploit the fact that the GMVP weights can be obtained as the population coefficients of a linear regression of one benchmark return on a vector of return differences. This 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 assets' conditional covariance structure, we model the portfolio weights through a Recursive Least Squares scheme as well as by Generalized Autoregressive Score type dynamics. Sparse parameterizations ensure scalability with respect to the number of assets. An empirical analysis of daily and monthly financial returns shows that the model performs well in-and out-of-sample in comparison to existing approaches.