Some properties of portfolios constructed from principal components of asset returns

IF 0.8 Q4 BUSINESS, FINANCE

Annals of Finance Pub Date : 2022-07-30 DOI:10.1007/s10436-022-00412-z

Thomas A. Severini

引用次数: 0

Abstract

Principal components analysis (PCA) is a well-known statistical method used to analyze the covariance structure of a random vector and for dimension reduction. When applied to an N-dimensional random vector of asset returns, PCA produces a set of N principal components, linear functions of the asset return vector that are mutually uncorrelated and which have some important statistical properties. The purpose of this paper is to consider the properties of portfolios based on such principal components, know as PC portfolios, including the efficiency of PC portfolios, the use of PC portfolios to reduce the return variance of a given portfolio, and the properties of factor models with PC portfolios as factors.

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由资产回报的主要组成部分构建的投资组合的一些性质

主成分分析（PCA）是一种众所周知的统计方法，用于分析随机向量的协方差结构和降维。当应用于资产回报的N维随机向量时，主成分分析产生了一组N个主成分，即相互不相关的资产回报向量的线性函数，它们具有一些重要的统计特性。本文的目的是考虑基于这些主成分的投资组合的性质，即PC投资组合，包括PC投资组合的效率，使用PC投资组合来减少给定投资组合的回报方差，以及以PC投资组合为因子的因子模型的性质。

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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来源期刊

Annals of Finance BUSINESS, FINANCE-

CiteScore

2.00

自引率

10.00%

发文量

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