Risk Budgeting Allocation for Dynamic Risk Measures

• 成果类型: Article
• 署名作者: Pesenti, Silvana M.; Jaimungal, Sebastian; Saporito, Yuri F.; Targino, Rodrigo S.
• 署名单位: University of Toronto; University of Oxford; Getulio Vargas Foundation
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0299
• 发表日期: 2025
• 关键词: Capital allocation; asset allocation; parity; portfolio

摘要

We define and develop an approach for risk budgeting allocation-a risk diversification portfolio strategy-where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalize the classical Euler contributions, which allows us to obtain dynamic risk contributions in a recursive manner. We prove that for the class of coherent dynamic distortion risk measures, the risk allocation problem may be recast as a sequence of strictly convex optimization problems. Moreover, we show that self-financing dynamic risk budgeting strategies with initial wealth of one are scaled versions of the solution of the sequence of convex optimization problems. Furthermore, we develop an actor-critic approach, leveraging the elicitability of dynamic risk measures, to solve for risk budgeting strategies using deep learning.