论文标题
基金模型增长的估计
Estimation of growth in fund models
论文作者
论文摘要
基金模型是对所有资产回报的市场的统计描述，这些资产收益均通过较低维度的资金收集（Modulo Orthoconal噪声）的回报。同等地，它们可能被描述为全球增长最佳投资组合仅涉及上述资金的投资的模型。在本地频繁估计下，基金模型中由于估计误差而导致的增长损失完全取决于资金数量。此外，在贝叶斯估计的一般过滤框架下，增长的损失与投资宇宙一样增加。提出了一种靶向最大生长的收缩方法，提出了最小偏差的最大生长方法。经验证据表明，收缩得出一个稳定的估计值，即比无限的贝叶斯估计更接近生长潜力。
Fund models are statistical descriptions of markets where all asset returns are spanned by the returns of a lower-dimensional collection of funds, modulo orthogonal noise. Equivalently, they may be characterised as models where the global growth-optimal portfolio only involves investment in the aforementioned funds. The loss of growth due to estimation error in fund models under local frequentist estimation is determined entirely by the number of funds. Furthermore, under a general filtering framework for Bayesian estimation, the loss of growth increases as the investment universe does. A shrinkage method that targets maximal growth with the least amount of deviation is proposed. Empirical evidence suggests that shrinkage gives a stable estimate that more closely follows growth potential than an unrestricted Bayesian estimate.
