Risk Budgeting Portfolios from Simulations

41 Pages Posted: 17 Mar 2022 Last revised: 2 Feb 2023

See all articles by Bernardo Freitas Paulo da Costa

Bernardo Freitas Paulo da Costa

Universidade Federal do Rio de Janeiro (UFRJ)

Silvana M. Pesenti

University of Toronto

Rodrigo Targino

Getulio Vargas Foundation (FGV) - EMAp - School of Applied Mathematics

Date Written: February 02, 2023

Abstract

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 returns for estimating risk budgeting portfolios. Besides a general cutting planes algorithm for determining the weights of risk budgeting portfolios for arbitrary coherent distortion risk measures, we provide a specialised version for the Expected Shortfall, and a tailored Stochastic Gradient Descent (SGD) algorithm, also for the Expected Shortfall. We compare our algorithm to standard convex optimisation solvers and illustrate different risk budgeting portfolios,
constructed using an especially designed Julia package, on real financial data and compare it to classical portfolio strategies.

Keywords: Portfolio Allocation, Risk Parity, coherent risk measures, Stochastic Optimisation

JEL Classification: G11, C58, C60

Suggested Citation

Freitas Paulo da Costa, Bernardo and Pesenti, Silvana M. and Targino, Rodrigo, Risk Budgeting Portfolios from Simulations (February 02, 2023). Available at SSRN: https://ssrn.com/abstract=4038514 or http://dx.doi.org/10.2139/ssrn.4038514

Bernardo Freitas Paulo da Costa

Universidade Federal do Rio de Janeiro (UFRJ) ( email )

Av; Pasteur, 250
terreo - Bairro Maracana
Rio de Janeiro, Rio de Janeiro 23890000
Brazil

Silvana M. Pesenti

University of Toronto ( email )

700 University Avenue 9F
Toronto, Ontario
Canada

Rodrigo Targino (Contact Author)

Getulio Vargas Foundation (FGV) - EMAp - School of Applied Mathematics ( email )

Praia de Botafogo
Rio de Janeiro, 22250-900
Brazil

HOME PAGE: http://rtargino.netlify.app/

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