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the Black–Litterman and reverse-optimization models

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NO.PZ201803130100000101

问题如下：

The asset allocation in Exhibit 1 most likely resulted from a mean–variance optimization using:

选项：

historical data.

reverse optimization.

Black–Litterman inputs.

解释：

A is correct.

The allocations in Exhibit 1 are most likely from an MVO model using historical data inputs. MVO tends to result in asset allocations that are concentrated in a subset of the available asset classes. The allocations in Exhibit 1 have heavy concentrations in four of the asset classes and no investment in the other four asset classes, and the weights differ greatly from global market weights. Compared to the use of historical inputs, the Black–Litterman and reverse-optimization models most likely would be less concentrated in a few asset classes and less distant from the global weights.

the Black–Litterman and reverse-optimization models

这两个模型不是解决MVO 对input敏感的问题吗？adding constraint 才是解决 concentrate的问题

嗨，爱思考的PZer你好：

MVO的缺点之一是分散化不足，配置的权重会集中于某些资产类型。表格中的模型与市场权重相比，模型的权重集中于US bonds，Emerging market equity和US equity 三大类资产，所以最有可能的就是用了MVO的方法。

而reverse optimization与Black-Litterman，由于这两种方法是通过已知权重、标准差、相关性，反向求出implied return，这里的权重通常使用的是资产的市值权重，所以求出来的implied return更稳定、更准确，因此得出的资产配置分散化效果也更好。

就这三个方法而言，reverse optimization与Black-Litterman方法得出的资产最优配置肯定是比MVO方法得出的配置分散效果好。但是跳出选项，要使组合配置的分散效果最好，绝对不过于集中在某一类或者某几类资产，那肯定是要用adding constraint 方法。

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the Black–Litterman and reverse-optimization models

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