Factor Risk Budgeting and Beyond

Adil Rengim Cetingoz, Olivier Guéant
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Abstract

Portfolio optimization methods have evolved significantly since Markowitz introduced the mean-variance framework in 1952. While the theoretical appeal of this approach is undeniable, its practical implementation poses important challenges, primarily revolving around the intricate task of estimating expected returns. As a result, practitioners and scholars have explored alternative methods that prioritize risk management and diversification. One such approach is Risk Budgeting, where portfolio risk is allocated among assets according to predefined risk budgets. The effectiveness of Risk Budgeting in achieving true diversification can, however, be questioned, given that asset returns are often influenced by a small number of risk factors. From this perspective, one question arises: is it possible to allocate risk at the factor level using the Risk Budgeting approach? This paper introduces a comprehensive framework to address this question by introducing risk measures directly associated with risk factor exposures and demonstrating the desirable mathematical properties of these risk measures, making them suitable for optimization. We also propose a framework to find the portfolio that effectively balances the risk contributions from both assets and factors. Leveraging standard stochastic algorithms, our framework enables the use of a wide range of risk measures.
要素风险预算及其他
自 1952 年马科维茨提出均值-方差框架以来,投资组合优化方法有了长足的发展。虽然这种方法的理论魅力毋庸置疑,但其实际应用却面临着重大挑战,主要是围绕着估计预期收益这一复杂任务。因此,从业者和学者们纷纷探索以风险管理和多样化为优先的替代方法。其中一种方法就是风险预算法,即根据预先确定的风险预算在资产间分配投资组合风险。然而,鉴于资产回报往往受少数风险因素的影响,风险预算法在实现真正多样化方面的有效性可能会受到质疑。从这个角度出发,就会产生一个问题:使用风险预算方法在因子层面分配风险是否可行?本文引入了与风险因子风险直接相关的风险度量,并证明了这些风险度量的理想数学特性,使其适用于优化,从而提出了一个综合框架来解决这个问题。利用标准随机算法,我们的框架可以使用各种风险度量。
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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