Asset and Factor Risk Budgeting: A Balanced Approach
ArXiv ID: 2312.11132 “View on arXiv”
Authors: Unknown
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? First, we introduce 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. Then, we propose a novel framework to find portfolios that effectively balance the risk contributions from both assets and factors. Leveraging standard stochastic algorithms, our framework enables the use of a wide range of risk measures to construct diversified portfolios.
Keywords: risk budgeting, factor allocation, stochastic algorithms, risk measures, portfolio optimization, Multi-Asset / Portfolio Management
Complexity vs Empirical Score
- Math Complexity: 8.5/10
- Empirical Rigor: 4.0/10
- Quadrant: Lab Rats
- Why: The paper introduces advanced mathematical constructs like custom risk measures with proofs of convexity and differentiability, and proposes stochastic optimization algorithms; while it includes two case studies, the summary and excerpt focus on theoretical framework development rather than extensive backtesting data or performance metrics.
flowchart TD
A["Research Goal<br>Can Risk Budgeting be applied<br>to factor allocations?"] --> B["Methodology<br>Introduce Factor Risk Measures<br>with desirable mathematical properties"]
B --> C["Input Data<br>Asset returns & Factor exposures"]
C --> D["Computational Process<br>Stochastic Optimization Algorithm<br>to balance asset & factor risk"]
D --> E["Outcome<br>Balanced Risk Budgeting Framework<br>for true diversification"]