论文标题
用于预测投资组合权重的动态条件方法
A dynamic conditional approach to portfolio weights forecasting
论文作者
论文摘要
我们从高频数据中构建了最佳实现的投资组合权重的时间序列，我们为动力学提出了一种新型的动态条件权重（DCW）模型。 DCW在权重预测和投资组合分配方面针对流行的基于模型和无模型的规格进行了基准测试。除了投资组合差异，确定性等效和营业额之外，我们引入了收支平衡的交易成本，这是一个额外的措施，以确定一种分配优于另一个分配的交易成本范围。通过比较Dow Jones 30指数的组成部分建立的最小值变化投资组合，拟议的DCW总体可以在任何程度的避免风险，交易成本和暴露范围内就考虑的措施获得最佳分配。
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.
