  We build the time series of optimal realized portfolio weights from
high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW)
model for their dynamics. DCW is benchmarked against popular model-based and
model-free specifications in terms of weights forecasts and portfolio
allocations. Next to portfolio variance, certainty equivalent and turnover, we
introduce the break-even transaction costs 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 overall attains the best allocations
with respect to the measures considered, for any degree of risk-aversion,
transaction costs and exposure.
