Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty
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Abstract:
We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix.
As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling.
We consider predictive distributions resulting from high-frequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others.
Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1/N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.

Downlad the paper: papers.ssrn.com/sol3/papers.cfm?abstract_id=3043216
Date: 23 February 2018, 14:15 (Friday, 6th week, Hilary 2018)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room B
Speaker: Nikolaus Hautsch (University of Vienna)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editors: Erin Saunders, Anne Pouliquen