A dynamic conditional approach to forecasting portfolio weights

Abstract From the autoregressive representation of the portfolio-variance optimization problem, we derive a novel model for conditional portfolio weights as a linear function of past conditional and realized (and, hence, observable) terms. This dynamic conditional weights ( DCW ) approach is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio allocations. Next to portfolio turnover and variance, we introduce the break-even transaction cost as an additional measure that identifies the range of transaction costs for which one allocation is preferred to another. By comparing minimum-variance portfolios built on the components of the Dow Jones 30 Index, the proposed DCW attains the best allocations overall with respect to the measures considered, for any degree of risk aversion, transaction costs, and exposure.