 A dynamic conditional approach to forecasting portfolio weightsFabrizio Cipollini, Giampiero Gallo () and Alessandro Palandri International Journal of Forecasting, 2021, vol. 37, issue 3, 1111-1126 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. Keywords: Portfolio allocation; Realized volatility; Realized correlations; Dynamic conditional modeling; Portfolio weights modeling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:3:p:1111-1126 DOI: 10.1016/j.ijforecast.2020.12.002 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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