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3543. Portfolio optimization based on multiperiod continuous stochastic dominance principles
Invited abstract in session MC-57: Dynamic portfolio selection: stochastic optimization, filtering, and learning techniques, stream Modern Decision Making in Finance and Insurance.
Monday, 12:30-14:00Room: S06 (building: 101)
Authors (first author is the speaker)
1. | Giorgio Consigli
|
Mathematics, Khalifa University of Science and Technology |
Abstract
We consider a multi-stage generalization of the interval-based stochastic dominance (ISD) principles introduced by Liu et al. (2021). The ISD criterion was motivated specifically in a financial context to
allow for contiguous integer SD orders on different portions of a portfolio return distribution against a benchmark distribution. A continuous spanning of SD conditions between first, second and third order stochastic dominance principles was introduced relying on a reference point. Here by extending the partial order to random data processes, we apply ISD conditions to a multi-period portfolio selection problem and verify the modeling and computational implications of such generalization. Several theoretical and
methodological issues arise in this case that motivate this contribution. The problem is formulated in scenario form as a multistage stochastic recourse program and we study two possible generalization of
ISD principles in which we either enforce ISD constraints on each stage, independently from the scenario tree process evolution, or we do so conditionally along the scenario tree. We present a comprehensive set
of computational results to show that, depending on the benchmark investment policy and the adopted ISD formulation, stochastic dominance conditions of first or second order can be enforced dynamically
over a range of possible values of the reference point and their solution carries a specific rationale.
Keywords
- Programming, Stochastic
- Financial Modelling
- Decision Theory
Status: accepted
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