Portfolio selection with higher moments
Portfolio selection with higher moments
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DOI：
10.1080/14697681003756877
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发表时间：
2010-01-01
影响因子：
1.3
通讯作者：
Mueller, Peter
中科院分区：
文献类型：
--
作者：
Harvey, Campbell R.;Liechty, John C.;Mueller, Peter
我们提出了一种使用贝叶斯决策理论框架进行最优投资组合选择的方法，该方法解决了传统马科维茨方法的两个主要缺点：处理更高矩和参数不确定性的能力。我们采用偏态正态分布，它具有许多吸引人的特征来建模多元回报。我们的结果表明，在投资组合选择中纳入高阶矩非常重要。此外，我们与忽略参数不确定性或以临时方式调节参数不确定性的其他方法进行比较表明，我们的方法比竞争方法（例如金融实践中常见的重采样方法）具有更高的预期效用。
We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than competing methods, such as the resampling methods that are common in the practice of finance.