Narrative-based robust stochastic optimization

R. Klerkx*, A. Pelsser

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Many portfolio optimization techniques rely heavily on past data and modeling assumptions. In an uncertain and ambiguous world, these techniques are prone to amplify model misspecification and therefore have poor out of sample results. Robust optimization explicitly recognizes uncertainty in model specification and performs better out of sample. The Achilles' heel of the method is the selection of the uncertainty set. In this paper we focus on the construction of the uncertainty set around the stochastic model specification. We propose to use narratives to select the elements in the uncertainty set to avoid using a logically inconsistent or too large uncertainty set. The narratives provide useful tools in a qualitative sense to the portfolio management process. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Original languageEnglish
Pages (from-to)266-277
Number of pages12
JournalJournal of Economic Behavior & Organization
Volume196
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • Optimization
  • Portfolio construction
  • Scenarios
  • Narratives
  • Fixed income
  • Uncertainty
  • OPTIMAL PORTFOLIO CHOICE
  • SELECTION

Fingerprint

Dive into the research topics of 'Narrative-based robust stochastic optimization'. Together they form a unique fingerprint.

Cite this