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Title: A time-varying parameter model with Bayesian shrinkage for global minimum variance portfolio prediction Authors:  Roman Liesenfeld - University of Cologne (Germany) [presenting]
Laura Reh - University of Cologne (Germany)
Guilherme Moura - Universidade Federal de Santa Catarina (Brazil)
Tore Selland Kleppe - University of Stavanger (Norway)
Abstract: A novel dynamic approach is proposed to high-dimensional portfolio selection based on predictions for the Global Minimum Variance Portfolio (GMVP). Using Bayesian regularization techniques, we aim to robustify the portfolios against estimation risk. Exploiting 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, we set up a linear state space model with time-varying parameters and stochastic volatility. This specification allows addressing both the time variation in the assets' conditional covariance structure and the heteroscedasticity in the market. Bayesian inference techniques with LASSO-type priors provide data-driven shrinkage to alleviate overfitting and to identify time-invariant coefficients automatically. Our approach allows for scalability to high dimensional applications and performs well in applications in which the number of observations per asset is low. The applicability and robustness of our approach are demonstrated through comprehensive simulation and empirical analysis. In particular, a simulation study shows that the proposed approach can perform better than the true model when the number of observations is not much larger than the number of assets. An application to daily financial returns also shows that the model performs better than a wide range of existing approaches in terms of out-of-sample forecasting accuracy.