Content area

Abstract

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 (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 sound 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.

Details

1009240
Title
Stochastic Algorithms for Advanced Risk Budgeting
Publication title
Source details
HAL, Working Papers
Publication year
2022
Publication date
2022
Publisher
Federal Reserve Bank of St. Louis
Place of publication
St. Louis
Country of publication
United States
Publication subject
Source type
Working Paper
Language of publication
English
Document type
Working Paper
ProQuest document ID
3126912330
Document URL
https://www.proquest.com/working-papers/stochastic-algorithms-advanced-risk-budgeting/docview/3126912330/se-2?accountid=208611
Copyright
©2022. Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the associated terms available at https://research.stlouisfed.org/research_terms.html .
Last updated
2024-11-12
Database
2 databases
  • ProQuest One Academic
  • ProQuest One Academic