风险预算投资组合:存在与计算

IF 1.6 3区 经济学 Q3 BUSINESS, FINANCE
Adil Rengim Cetingoz, Jean-David Fermanian, Olivier Guéant
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引用次数: 0

摘要

几十年来,现代投资组合理论一直是优化投资组合的主要框架。马科维茨在 1952 年提出的均值-方差框架由于对输入参数(尤其是预期收益)的微小变化非常敏感,因此受到了纯粹基于风险的新构建方法的挑战。在基于风险的方法中,最流行的是最小方差法、最大分散法和风险预算法(尤其是等风险贡献法)投资组合。尽管有一些缺点,风险预算法因其多功能性而特别吸引人:基于欧拉同质函数定理,它确实可以用于各种风险度量。本文介绍了有关风险预算投资组合对于多种风险度量的存在性和唯一性的数学结果,并说明对于其中许多风险度量,计算风险预算投资组合的权重只需要一个标准的随机算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk Budgeting portfolios: Existence and computation

Modern portfolio theory has provided for decades the main framework for optimizing portfolios. Because of its sensitivity to small changes in input parameters, especially expected returns, the mean–variance framework proposed by Markowitz in 1952 has, however, been challenged by new construction methods that are purely based on risk. Among risk-based methods, the most popular ones are Minimum Variance, Maximum Diversification, and Risk Budgeting (especially Equal Risk Contribution) portfolios. Despite some drawbacks, Risk Budgeting is particularly attracting because of its versatility: based on Euler's homogeneous function theorem, it can indeed be used with a wide range of risk measures. This paper presents mathematical results regarding the existence and the uniqueness of Risk Budgeting portfolios for a very wide spectrum of risk measures and shows that, for many of them, computing the weights of Risk Budgeting portfolios only requires a standard stochastic algorithm.

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来源期刊
Mathematical Finance
Mathematical Finance 数学-数学跨学科应用
CiteScore
4.10
自引率
6.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.
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