Risk Budgeting Allocation for Dynamic Risk Measures

Silvana M. Pesenti (), Sebastian Jaimungal (), Yuri F. Saporito () and Rodrigo S. Targino ()
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Silvana M. Pesenti: Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5G 1Z5, Canada
Sebastian Jaimungal: Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5G 1Z5, Canada; and Oxford-Man Institute, University of Oxford, Oxford OX2 6ED, United Kingdom
Yuri F. Saporito: School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro 22250-900, Brazil
Rodrigo S. Targino: School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro 22250-900, Brazil

Operations Research, 2025, vol. 73, issue 3, 1208-1229

Abstract: 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.

Keywords: Financial Engineering; dynamic risk measures; portfolio allocation; risk parity; elicitability; deep learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:73:y:2025:i:3:p:1208-1229

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