Deep Declarative Risk Budgeting Portfolios
Recent advances in deep learning have spurred the development of end-to-end frameworks for portfolio optimization that utilize implicit layers. However, many such implementations are highly sensitive to neural network initialization, undermining performance consistency. This research introduces a robust end-to-end framework tailored for risk budgeting portfolios that effectively reduces sensitivity to initialization. Importantly, this enhanced stability does not compromise portfolio performance, as our framework consistently outperforms the risk parity benchmark.
Code (0)
등록된 구현이 없습니다.
Tasks
Portfolio OptimizationSensitivitySimilar Papers 제목 키워드 기반
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 pro…
Risk Budgeting Portfolios from Simulations
Risk budgeting is a portfolio strategy where each asset contributes a prespecified amount to the aggregate risk of the portfolio. In this work, we propose an efficient numerical framework that uses only simulations of re…
Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles
This article develops the theory of risk budgeting portfolios, when we would like to impose weight constraints. It appears that the mathematical problem is more complex than the traditional risk budgeting problem. The fo…
Mirror Descent Algorithms for Risk Budgeting Portfolios
This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent algorithms to determi…
Asset and Factor Risk Budgeting: A Balanced Approach
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 im…
ManagementPortfolio Optimization