Return Prediction and Portfolio Selection: A Distributional Approach

Min Zhu
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Abstract

The inquiries to return predictability are traditionally limited to the first two moments, mean and volatility. Analogously, literature on portfolio selection also stems from a moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regression and copulas, using the quantile approach to extract information in marginal distributions and copulas to capture dependence structure. A nonlinear utility function is proposed for portfolio selection which utilizes the full underlying return distribution. An empirical application to US data highlights not only the predictability of the stock and bond return distributions, but also the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.
收益预测与投资组合选择:一种分布方法
传统上,对可预测性的查询仅限于前两个时刻,即均值和波动性。类似地,关于投资组合选择的文献也源于基于时刻的分析,最多考虑到第四个时刻。本文开发了一个基于分布的收益预测和投资组合选择框架。更具体地说,通过分位数回归和copula对时变收益分布进行建模,使用分位数方法提取边缘分布和copula中的信息来捕获依赖结构。提出了一种非线性效用函数用于投资组合选择,该函数利用了全部潜在收益分布。对美国数据的经验应用不仅突出了股票和债券回报分布的可预测性,而且还突出了分布方法提供的传统基于矩的方法无法捕获的附加信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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