An L-Moment Approach for Portfolio Choice under Non-Expected Utility
提出一种基于L-矩的半参数估计方法，该方法对异常值稳健且能处理传统矩不存在的情况。实证表明，该方法在非期望效用下的投资组合选择中能有效应对估计风险，产生稳定的组合收益并利用高阶矩信息。
Abstract We develop and apply a novel semi-parametric estimation method based on L-moments. Unlike conventional moments, L-moments are linear in the data and therefore robust to outliers. The estimation method provides a series expansion that quickly converges to the underlying return distribution and can be used when conventional moments do not exist. An extensive empirical analysis of portfolio choice under non-expected utility demonstrates the effectiveness of our approach. Empirical results show that our method copes well with estimation risk, yields stable portfolio returns, and reaps the information content of moment returns beyond order four.